Leveraging Graphical Models to Improve Accuracy and Reduce Privacy Risks of Mobile Sensing

13 Pages·2013·0.62 MB·English

by  Abhinav Parate

Leveraging Graphical Models to Improve Accuracy and
Reduce Privacy Risks of Mobile Sensing
Abhinav Parate Meng-Chieh Chiu
Deepak Ganesan Benjamin M. Marlin
DepartmentofComputerScience
UniversityofMassachusetts,Amherst
Amherst,MA01003-9264
{aparate,joechiu,dganesan,marlin}@cs.umass.edu
ABSTRACT Keywords
Theproliferationofsensorsonmobilephonesandwearableshas Continuous context-sensing; Energy-Accuracy-Privacy optimiza-
ledtoaplethoraofcontextclassifiersdesignedtosensetheindivid- tions;Mobilecomputing
ual’scontext. Wearguethatakeymissingpieceinmobileinfer-
enceisalayerthatfusestheoutputsofseveralclassifierstolearn
CategoriesandSubjectDescriptors
deeper insights into an individual’s habitual patterns and associ-
atedcorrelationsbetweencontexts,therebyenablingnewsystems C.5.3 [Computer System Implementation]: Microcomputers—
optimizations and opportunities. In this paper, we design CQue, Portabledevices;D.4.8[OperatingSystems]:[PerformanceMod-
adynamicbayesiannetworkthatoperatesoverclassifiersforindi- elingandprediction];K.8[PersonalComputing]:[General]
vidualcontexts,observesrelationsacrosstheseoutputsacrosstime,
andidentifiesopportunitiesforimprovingenergy-efficiencyandac- 1. INTRODUCTION
curacybytakingadvantageofrelations. Inaddition,suchalayer
The past decade has seen unprecedented growth in sensor-rich
providesinsightsintoprivacyleakagethatmightoccurwhenseem-
mobilephonesandwearableaccessoriessuchasfitnessmonitors,
inglyinnocuoususercontextrevealedtodifferentapplicationson
sleep monitors, heart monitors and others. With the proliferation
aphonemaybecombinedtorevealmoreinformationthanorigi-
ofsuchdevices,therehasbeensignificantemphasisontechniques
nallyintended. Intermsofsystemarchitecture,ourkeycontribu-
todrawhigher-levelinferencesfromcontinuoussensordataaswe
tionisacleanseparationbetweenthedetectionlayerandthefusion
movearoundinourday-to-daylives.Researchhasshownthatsev-
layer,enablingclassifierstosolelyfocusondetectingthecontext,
eralaspectsofourbehaviorcanbeinferredincludingphysicalac-
andleveragetemporalsmoothingandfusionmechanismstofurther
tivity,sleepbehavior,socialcontext,movementpatterns,emotional
boostperformancebyjustconnectingtoourhigher-levelinference
oraffectivecontext,andmentaldisorders,withvaryingdegreesof
engine. To applications and users, CQue provides a query inter-
accuracy.
face, allowinga)applicationstoobtainmoreaccuratecontextre-
Thegrowinglandscapeofhigh-levelinferencespresentsanin-
sultswhileremainingagnosticofwhatclassifiers/sensorsareused
teresting opportunity: can we combine these inferences to obtain
andwhen,andb)userstospecifywhatcontextstheywishtokeep
deeper insights into individual behavior? Intuition suggests that
private,andonlyallowinformationthathaslowleakagewiththe
the outputs of individual inference algorithms must be correlated
privatecontexttoberevealed. WeimplementedCQueinAndroid,
acrossspaceandtime; afterall,theysensevariousdimensionsof
andourresultsshowthatCQuecani)improveactivityclassifica-
an individual’s habits, behaviors and physiology all of which are
tionaccuracyupto42%,ii)reduceenergyconsumptioninclassify-
inter-linked. Asasimpleexample,takethecaseofanindividual’s
ingsocial,locationandactivitycontextswithhighaccuracy(>90%)
mobilitypatternsandhowmuchofitcanbeinferredwithoutusing
byreducingthenumberofrequiredclassifiersbyatleast33%,and
anylocationsensorsuchasGPS,celltowerorWiFi.Locationisof-
iii)effectivelydetectandsuppresscontextsthatrevealprivatein-
tencorrelatedtosocialcontext—thefactthatphonesofcolleagues
formation.
areinproximity(detectedviabluetooth)canindicatethatthelikely
locationistheworkplacewhereasthefactthatafamilymember’s
phone is nearby means that one is likely at home. Similarly, lo-
cationrelatestoactivitycontext—ausermaybemoresedentary
atworkthanatothertimesduringtheday,thereforeonecouldin-
ferthatthemostlikelylocationistheworkplacebyobservingthe
Permissiontomakedigitalorhardcopiesofallorpartofthisworkfor levelofactivity. Morebroadly,almostallinferencesarerelatedin
personalorclassroomuseisgrantedwithoutfeeprovidedthatcopiesare one way or another — studies have shown that sleep (or lack of
notmadeordistributedforprofitorcommercialadvantageandthatcopies it)affectsourmoodandproductivity,socialinteractionsinfluence
bearthisnoticeandthefullcitationonthefirstpage.Tocopyotherwise,to ouremotionalcontext,addictivebehavioriscorrelatedtospecific
republish,topostonserversortoredistributetolists,requirespriorspecific
locationsandsocialinteractions,andsoon
permissionand/orafee.
Theexistenceofarichinteractiongraphbetweeninferencespresents
MobiSys’13,June25-28,2013,Taipei,Taiwan
Copyright2013ACM978-1-4503-1672-9/13/06...$15.00. anopportunityandachallenge. Ononehand, amodelofthere-
lationsacrossthedifferentinferencescanbeleveragedtogainin- specificlocationcontextthattheuserdoesnotwishtoreveal. Fi-
sightsintoindividualbehavior,therebyenablingnewsystemsopti- nally,throughprivacypolicies,ausercanspecifyacontextaspri-
mizationopportunities.Forexample,ahighlevelinferenceframe- vatethatshouldnotberevealed,andsuppressthenon-privatecon-
workmightobserverelationsbetweensemanticlocationandactiv- texts in real-time that can be used to infer private context using
itylevels,andlearnthatanindividualhasmostlysedentarybehav- correlationsamongthesecontexts. Ausercanspecifythispolicy
iorattheworkplace. Thisprovidestwoopportunitiestooptimize specifictoanapplicationoragroupofapplicationsthatcanpoten-
inferenceofthelocationcontext,“work": a)energy-efficiencycan tiallycollude.
beimprovedbyrelyingprimarilyonactivityinferenceratherthan Ourresultsshowthat:
expensiveGPS,andb)accuracycanbeimprovedwhenthereissig-
CQuecananswercontextquerieswithhighconfidenceand
nificanterror(e.g.indoorsettings)byfusingitwithactivitylevels. •
improveaccuracyupto42%byperformingfusionofinfor-
Ontheotherhand,theabilitytoleveragerelationsacrossinfer-
mationfrommultiplecontext-inferencealgorithms
ences comes with a steep price tag — loss of privacy. A mobile
application that purports to merely be using the accelerometer to Whenenergyislimited,CQuecanlowerexecutioncostsfor
detectactivitylevelsmayindeedbeinferringyourlocation. More • multiple context queries by exploiting context relations to
disturbingisthepossibilitythatasingleapplicationdevelopermay runfewerinferencealgorithms. Ifenergyisplentiful,CQue
have several applications, one that monitors sensors for activity candecidewhatcontextalgorithmsinadditiontothequery
level,andperhapsanotherthatusesbluetooth,whichmaybelever- setshouldbeexecutedtoimproveaccuracy.
agedinconjunctiontorevealmuchmorethanfromtheseemingly
innocuousindividualapplications. Compoundingthisissueisthe CQueiseffectiveinassessingprivacyrisksandprovidespri-
•
fact that we do not have tools that allow us to reason about how vacywhileensuringhighutilitytotheapplications.
muchofprivacyexposureoccursfromrevealingseeminglyinnocu-
Therestofthepaperisorganizedasfollows. §2describesthe
oussensordataiftheadversaryweretohaveagoodmodelofthe
prior work done in the related research areas. In §3, we provide
relationsacrosscontexts.
an high-level overview of the CQue query engine along with the
In this paper, we present a mobile “big data” inference frame-
descriptionofhowacontextquerycanbespecifiedinourframe-
work, CQue, that takes as input streams of inferences from di-
work. In§4.1,wedescribetherelationshipmodelusedbyCQue.
verseon-bodyandsmartphone-basedsensors,andprovidesauni-
Restof§4describesthecomponentsofCQuequeryengineinde-
fiedwayforexploitingandexploringrelationsacrosstheseinfer-
tailthatareresponsibleforoptimizingmultiplecontextqueriesand
ences.CQuecanbeusedinseveralways:a)aninferencealgorithm
addressingprivacy.Wedescribetheimplementationdetailsandthe
canleverageCQuetoimproveaccuracyand/orenergy-efficiency,
experimentalevaluationofCQuein§5and§6respectively.Wedis-
whileremainingagnosticofhowthisisachieved,b)ausercanis-
cusspossibleextensionsofthisworkin§7.Finally,theconclusions
sue“whatif"queriesthatexploretheextentofprivacyleakageof
areprovidedin§8.
sensitiveinformationthatmightbepossibleifcertainsensordata
wererevealedtooneormoreapplications,andc)ausercanspecify
2. RELATEDWORK
privacypoliciestoprovideafirstorderprotectionagainstprivacy
leakageofsensitiveinformationtotheuntrustedapplications. Inthissection,wedescribethreeareasofrelatedwork—model-
Intermsofsystemarchitecture,thekeybenefitofCQueisthatit drivensensordataacquisition,contextinferenceformobilephones
separatesdetectionfromfusion. Existingclassifiersarelargelyde- andprivacyintemporaldata.
signedinastovepipemannertoaddressaspecificcontextsensing Graphicalmodelsforsensordataacquisition: Therehasbeen
goalasbestaspossible.Whiletheystartwithdetectionofthespe- substantialworkonleveragingspatialandtemporalmodelsofcor-
cificactivity(e.g. conversation,walking,etc),real-worldvagaries relationsbetweendistributedsensorsourcestooptimizesampling
oftenresultinspuriousstatetransitionsduetoseveralconfounding andcommunicationinasensornetwork[3,5,6,12,14]. Forex-
factors. Toaddresstheseissues,classifiersoftenrelyonothersen- ample, BBQ [6] uses a Dynamic Bayesian Network to select the
sorsourcesthatcanidentifyconfoundersandreduceerrors. CQue minimumnumberofsensornodesfordataacquisitionsuchthatit
providesacleanseparationbetweenthedetectionlayerandthefu- cananswerrangequerieswithinquery-desiredconfidencebounds.
sionlayer—inthisway,aclassifiercanbedesignedtojustfocus Meliouetal[14]extendthisworktosensornetworkroutingwhere
ondetectingthephenomenaofinterest,andleveragefusionmech- a query can be answered with desired confidence bounds while
anismstofurtherboostperformancebyjusthookingintoCQue. traversing the minimum number of nodes in a network spanning
Tousersandapplications,CQueoffersasimplecontextquery- tree. Graphical models are also used in [5], which explored the
inginterfacewithsupportforseveraltypesofqueries. A“context problemofansweringrangequerieswhileminimizingtheenergy
query"canrequestdifferentcontexts, whileoptionallyspecifying costofsamplingsensors,andin[3],wheresensorsusemodelsto
constraintssuchasconfidencerequirementsanddelaybounds.For reducethecommunicationcostsbytransmittingsamplestoabase
example,aquerymightrequestsedentaryactivitycontextwith90% station only when the ground truth is significantly different from
certaintyandamaximumnotificationdelayoftwominutes. CQue thepredictionmadebythemodel. Thesimilaritieswithprioref-
uses the query constraints to reason about how to duty-cycle in- forts are only in that they leverage DBNs — we use DBNs in a
ferencealgorithms,andhowmuchtemporalhistorytoleverageto novelapplicationcontextwhichisforreal-timeinferenceonamo-
improve accuracy and confidence. A “what if" query provides a bile phone to enable energy-accuracy-delay tradeoffs and protect
measure of potential leakage of sensitive information if applica- privacy.
tionswereallowedaccesstospecificsensorsources. Forexample,
Context-Sensing: OurworkisclosesttoACE[15]thatproposes
aquerycanrequesttheexpectedleakageof“home"locationcon-
acontextsensingenginethatexploitsrelationshipsamongcontexts
text if an application had access to accelerometer and bluetooth
toinferanenergy-expensivecontextfromacheapercontext. The
data. Internally, CQue would execute such a query by providing
centraldifferencebetweenACEandCQueisthattheformeruses
a measure of the correlation between the outputs of inference al-
AssociateRuleMiningtolearnrulesamongcontexts(e.g.Driving
gorithmsthatoperateonaccelerometerandbluetoothdata,andthe
AtHome),butsuchminingapproachescannottakeacontext
)¬
classifier’suncertaintyintoaccount.Theabilitytotakeuncertainty Query Set
into account is critical when using classifiers that operate on raw Q1: (drive | accel+gyro), (walk | accel+gyro), (stationary| accel+gyro)
Q2: (with friends | bluetooth), (with spouse | bluetooth)
sensordata—forexample,ifaclassifiermaydetectDrivingwith Q3: (at-office | wifi+gps,120s,0.9)
alowconfidence,say0.7,GPSmaydetectAtHomewithanerror
radiusof100m,andsoon. Inthesecases,theruleswouldassume Fitness App Movie Recommender Do not
App Disturb App
perfect context information whereas CQue would use the uncer-
taintyininferringtherelations. Thus, aprobabilisticapproachis Q1 Q2 Q3
strictlysuperiortoarulebasedone.
AlsorelatedtoCQueareeffortstodevelopacontext-sensingen-
Query Interface
gineforphonethatcanbeusedbyapplicationstorequestcontexts offload dynamic
[4,13]. TheJigsawcontextsensingengine[13]comprisesofaset user privacy plan evaluation
settings Dynamic Query Execution Engine to cloud
ofsensingpipelinesforaccelerometer,microphoneandGPS.More (cost- accuracy - privacy optimization)
recently,Kobe[4]proposedacontextqueryingengineformobile
offload DBN
phonesthatcanbeusedtoplugindifferentclassifiers,andthatbal- learning to
ancesenergyconsumption,latencyandaccuracyoftheclassifiers Dynamic Bayesian Network cloud
byoffloadingcomputationtothecloud. Unliketheseapproaches,
CQuecanleverageprobabilisticrelationsacrosscontexts.
CQue is complementary to prior work on context sensing en- S1 S2 S3
gineswhichhavelargelyexploredoptimizationsofindividualclas- H1 Walking H2 Driving H3 AtHome
sifiers(e.g. [2,11,13,20]). CQueallowsindividualclassifiersto
beeasilyintegratedwithoutworryingabouthowtoleverageother Sensors
contextstoimproveperformanceandefficiency.
Privacy in temporal data: There has been some work in ad-
dressingprivacywheretheadversaryisawareofthetemporalcor- Figure1:High-leveloverviewoftheframeworkdescribingthe
relations [8, 17, 19]. MaskIT [8] presents a privacy-preserving workflow
mechanismtofilterastreamofcontextsthatcanbeusedtoanswer
context queries requested by the phone applications. It presents
twoprivacycheckstodecidewhenausercontextcanbereleased anycombinationofclassifiersthatusethosesensorsmaybeused
whileprovidingprivacyguaranteesagainstanadversaryknowing inansweringthequery(toensurethattheapponlyusessensorsfor
temporalcorrelationsmodeledasaHiddenMarkovmodel(HMM) whichithasapprovalfromtheuser). Thesekeywordscouldalso
ofusercontexts. refertootherinferences,iftheapplicationneedsmoreexplicitcon-
Our goal in this work is not to to prove privacy guarantees or troloverothercontextsthatshouldbefused.Finally,thequerycan
designnewprivacymetrics,ratherourargumentisthattheuseof also replace this with a ‘*’ which would let CQue automatically
DBNenablesconsiderablymoreflexibleprivacypoliciesincom- choose sensors and classifiers to optimize the system. The value
parison with an HMM-based approach. When MaskIT identifies ‘240s’inthequeryisthedelaytoleranceanddenotesthattheap-
thataprivacybreachmightoccur,itsuppressesallcontextsatthat plicationcantoleratethemaximumdelayof240secondsforeach
timewithoutconsideringwhichsubsetofthemmightcontributeto recordinthequeryresponse. Thequeryprocessorusesthisdelay
thebreach. Incontrast,CQueenablesmorefine-grainedreasoning windowtoimprovetheconfidenceintheoutputcontextvalue.The
ofwhichspecificcontext(s)leadtoaprivacybreachsuchthatonly querycanalsoprovideaconfidencethreshold. Intheaboveexam-
thatinformationcanbesuppressed. ple, the query processor will output context as soon as it reaches
confidence of ‘0.8’, provided it is within the delay tolerance pe-
3. CQUEOVERVIEW riod. If confidence values associated with the context output are
lower than the query-desired confidence threshold, the context is
The CQue framework has been designed with the goal of pro-
reportedas‘unknown’.
vidinganeasy-to-useabstractiontoapplicationdevelopersandend
Thenexttwoqueriesmarkedby’?’areprivacyqueriesandpro-
users,bothforexploitingcorrelationsacrossinferencetoimprove
vide a measure of information gain in queried context "home" if
energy-efficiencyandaccuracy,aswellastousethesecorrelations
any inference algorithm operating over accelerometer and gyro-
toascertainprivacyleakageorcontextualinsights.Thisgoalisac-
scopesensorswereused(Q2)orifwithfriendsinferencealgorithm
complished using a high level query language and an underlying
wasused(Q3). Inturn,thismeasureprovidesaprivacyscore,an
execution environment running as a service on the phone. In the
indicatorofprivacyleakage.Notethatinprovidingaprivacyscore,
following,weprovideanhigh-leveloverviewoftheCQueframe-
CQueislimitedtousinginferencealgorithmsthatareavailablein
work.
itslibrary.
Context Query Interface. At the top level of CQue are ap- CQue Architecture. ThecentraltenetofCQueisanarchitec-
plicationsthatissuecontextqueriesusingahigh-leveldeclarative
turalseparationbetweenthemechanismsthatinferencealgorithms
querylanguage.CQueprovidesasimplequerylanguagefortheap-
use to improve performance. Inference algorithms are often de-
plicationstorequestcontexts.Considerthefollowingthreequeries:
signed in a stovepipe manner and integrate a combination of the
following components: a) feature extraction, which involves ex-
Q1. (drive | accel+gyro+gps, 240s, 0.8)
Q2. (home? | accel+gyro) tracting time-domain or frequency-domain features from the raw
Q3. (home? | with friends) sensor data, b) classification, where they look at a short tempo-
ral sequence and detect a particular event or state, for example,
The first query requests the context drive, and includes three walking, running, cycling, smoking, etc, c) temporal consistency
otheroptionalfields.Thekeywordsaccel,gyroandgpssuggestthat mechanisms such as Hidden Markov Models (HMMs) to correct
mis-classifications that occur in the classification, and d) context
X1,t-1 X1,t
fusion where they leverage other information such as location or
timetoprovideadditionalinputtocorrectclassificationerrors. X2,t-1 X2,t
Incontrasttothestovepipeapproach,ourdesignseparatesthese
X3,t-1 X3,t
componentsintoacleaner,layeredarchitecturewhereanyclassifier
cansimplyhookintoCQue,andleveragetemporalconsistencyand Time t-1 Time t
contextfusionmechanismstoimproveperformance.CQuemodels
temporalconsistencywithatime-seriesofpreviousobservations,
Figure 2: An example of 2-step Dynamic Bayesian Network.
and uses active learning mechanisms to automatically determine
ThenodesinthegraphmodelthesetofrandomvariablesX=
whenuserinputisneededtoimprovethismodel. CQuealsoauto-
X1,X2,X3 .
maticallydeterminestherelationshipsbetweenaclassifieroutput { }
andallotherobservationsfromotherclassifiers/sensorsthatithas
accessto, anddetermineswhenandhowtouseadditionalobser- amongrandomvariablesatdifferenttimeinstances. Thismodelis
vationstoimproveaccuracy. Inshort, itcanmaketheprocessof representedasadirectedacyclicgraphconsistingofnodescorre-
designinganewinferencealgorithmaloteasierforadesigner. spondingtoeachrandomvariableateachtimeinstance.Thestatic
dependenciesacrossrandomvariablesattimeinstancetarerepre-
ExecutionEnvironment. TheexecutionenvironmentofCQue sentedbyedgesconnectingnodescorrespondingtotheserandom
isshowninFigure1andconsistsoffollowingcorecomponents:(i) variables.Thetemporaldependenciesarerepresentedbytransition
agraphicalmodel,namely,DynamicBayesianNetwork,and(ii)a edgesconnectingnodesattimeinstancet 1withnodesattime
�
queryprocessor. TheDynamicBayesianNetwork(DBN)istheat instance t. Each node in this graph has a conditional probability
thecoreofCQueandplaysanimportantroleinmakingvariousde- table(CPT)describingdependenciesonitsparentnodes. Figure2
cisionsduringqueryprocessing.TheDBNprovidesamodelofthe showsanexampleof2-stepDBNfortwotimesliceswhichcanbe
relationshipsacrossmultiplecontextsforthephoneuserandkeeps unrolledtoaccommodateatime-seriesofanylengthbyduplicating
trackofthetime-seriesofobservationsforvariouscontexts.Itthen thetime-slicesandtransitionedges. WeusenotationXtodenote
usescontextrelationshipstoboostconfidenceinobservationsmade thesetofrandomvariables.WeuseXttodenotesetofnodesrep-
byindividualclassifiersortocorrectthem.Thesecondcomponent resentingrandomvariablesXattimeinstancetanduseX1...t as
isaqueryprocessorwhichisresponsibleforinteractionwithappli- shortnotationtodescribetimeseriesofnodesX1,...,Xt. With
cationsincludingreceivingcontextqueriesandgeneratingthere- thisnotation,thejointprobabilitydistributionforatimeseriesof
sponsestothequeries. Inaddition,dependingonthecurrentstate nodesisgivenby
oftheDBNandthetime-seriesofobservations,thequeryproces-
t
sorneedstodeterminewhichcontext-inferencealgorithmsshould
be executed such that it can provide best answers for the queries Pr(X1...t)=Pr(X1) Pr(Xt|Xt�1)
withinthespecifiedconstraints. Thesedecisionsaremadebythe tY=2
queryprocessorateverytimestepduringexecution. Asshownin In the above equation, probability Pr(Xt Xt 1) is computed
| �
Figure1,theresultofthesedecisionsarethesignalsS1,S2,...,Sn usingtheDBN.
indicatingwhichcontext-algorithmsbeexecuted.Finally,uponob-
servingtheoutputofselectedcontext-inferencealgorithms,query DBNModelforCQue. InCQue,wehavethreetypesofDBN
processorenforcesuser-specifiedprivacypolicies(definedin§4.2) nodes—sensors,classifier-providedcontext,andreal-worldcon-
bydecidingamaximalsetofqueriedcontextswhosevaluescanbe text. The goal of the DBN is to model relationships across real-
releasedtotheapplicationswithoutviolatingtheprivacypolicies. worldcontextswhiletakingintoaccounttheuncertaintyassociated
Inadditiontothequeryexecution,thequeryprocessorisrespon- with the classifier-provided contexts. We considered two criteria
sibleforsamplinghuman-providedcontextvaluesduringthelearn- inconstructingtheDBNmodel: a)classifiersareblackboxes,and
ingphaseoftheDBN.Suchhumaninputcanbeminimizedthrough we do not assume knowledge of what sensors they utilize, what
activelearningtoonlyobtaininputatappropriatetimestoimprove sampling rates they use, how they duty-cycle sensors to save en-
the structure of the DBN. These human inputs shown as signals ergy, and what features they extract, and b) we wish to keep the
H1,H2,...,Hn in Figure 1 are provided to the context-inference model simple and low complexity so that DBN inference can be
algorithmsthatcanuseittoretrainthemselvestopersonalizefor performedinreal-timeonthemobilephone.
thephoneuser. Toaddresstheseconsiderations,classifiersandsensorsaresepa-
ratedfromtheDBNintoadifferentlayer.Figure3showsanexam-
4. THEEXECUTIONENVIRONMENT ple where classifiers provide the probability of a context through
observationnodescalledvirtualevidences,O,totheDBN.Theun-
TheexecutionenvironmentofCQueisresponsiblefori)mod-
certaintyinevidenceisaccountedintheseobservationnodesusing
elingandlearningtherelationshipsacrosscontexts,ii)multi-query
optimizationsuchthatuncertaintyinqueriedcontextsisminimized Pearl’smethod[18]. Inthismethod,forsomecontextXiandcor-
whileoperatingwithinthebudgetanddelayconstraintsofaquery, responding observation node Oi, if L(x) gives the likelihood of
and iii) executing privacy queries and enforcing privacy policies. classifier stating that Xi = x if Xi is actually in state x and if
Inthefollowing,wedescribeeachcomponentoftheexecutionen- L(x)givesthelikelihoodofaclassifierstatingthatXi = xifXi
vironmentindetail. isnotinstatex,thentheconditionalprobabilitiesforobservation
nodeOigiventhecurrentstateforrealworldcontextXisatisfies:
4.1 InferenceFramework
Pr(O =xX =x):Pr(O =xX =x)=L(x):L(x)
The core learning framework in CQue is a Dynamic Bayesian i | i i | i 6
network(DBN),whichisaclassofBayesiannetworksthatcanrep- Withtheseconditionalprobabilitiesandgivenaseriesofvirtual
resentatime-seriesofrandomvariablestomodeltemporalconsis- evidencesO1...t, wecancomputethejointprobabilitiesfortime
tency. ADBNcandescribebothtemporalandstaticrelationships seriesofrealworldcontextsX1...tasfollows:
a)howtochoosetheinferencealgorithmstoexecutegiventhebud-
X1
t-1 X2 t+1 get?,andb)howtohandledifferentdelaytolerancerequirements
ofqueries? Inaddition,privacyqueriesandpoliciesrequireusto
answer:a)howtoexecuteprivacyqueries?,andhowtoenforcepri-
O1 O2 Virtual vacypolicies?Weaddressthesequestionintherestofthissection.
Evidences
Whichinferencestoexecutegivenabudget?
Time t C1 C2
The CQue query processor needs to execute the set of inference
algorithmsthatprovidesmaximumvalueofinformation(VOI)for
S1 S2 S3 thequeriedcontextswithinthebudgetconstraints. Thebudgetis
typicallyintheformofenergysinceinferencealgorithmsconsume
energy,butincaseswheretheuserisinterruptedtoprovidelabels,
Figure 3: A model separating DBN from the layer of sensors
thebudgetmaybethenumberofinterruptionsallowedperday.
andclassifiers,whereclassifiersprovidevirtualevidencestothe
Duetotherelationshipsacrosscontexts,thesetprovidingmax-
DBN.
imumVOImaybelargerthanthequerysetassomecontextsthat
arenotpartofthequerysetmaybeusefultoimprovetheinference
accuracyforcontextsthatareinthequeryset. Similarly, thisset
canbesmallerthanthequerysetifsomeofthequeriedcontexts
t
Pr(X1...t,O1...t)=Pr(X1O1) Pr(XtXt 1)Pr(OtXt) are strongly implied by other contexts in the query. In addition,
| tY=2 | � | thesetofinferencesthatprovidesthemaximumVOIcanchange
overtime, dependingontheuser’scurrentcontextaswellasdy-
Thismodelhasseveralbenefits:a)itiscomputationallycheapboth
namicsintheuser’sbehaviorpatterns. Inthissection,wedescribe
whenclassifiersareturnedonoroffsinceitavoidsthecomplexity
anadaptiveapproachtodecidingthesetofinferencesthatneedto
ofmodelingsensorandclassifiervariablesintheDBNitself,b)it
beexecutedtosatisfyquerydemandswhileremainingwithinthe
requires no additional human labels for modeling these variables
budget.
whiletrainingtheDBN,andc)itcaneasilybeextendedtohandle
Theproblemofselectingtheoptimalsetofcontext-algorithms
multiple classifier implementations for the same context without
toexecutegivenabudgetandthesetofqueriescanbeformalized
changingtheDBNstructure.
as follows. Let Q be the set of context queries, let Et 1 be the
�
setofcontextclassifiersundergoingexecutioncurrentlyandv be
DBNusageinCQue. TheoutputoftheDBNisusedinseveral
waysbyCQue. thecorrespondingclassifieroutputs. LetF(Q,E,Et�1,v)bethe
utility function giving the VOI of the contexts, E, for the query
1. Filtering/Smoothing Most of the contexts that we observe setwhereEisthesubsetofobservationnodesOintheDBN.Let
in the real world have some temporal consistency i.e they C(E)givethecostofexecutingalgorithmsforcontextsinE.If
B
lastforsometimeperiod. Suchtemporalrelationshipscan isthemaximumbudget,thenourgoalcanbedefinedasidentifying
be used to correct intermittent misclassifications made by asetEt i.e. thesetofcontextclassifierstobeexecutednextsuch
context-inferencealgorithms. SinceaDBNmodelstempo- that:
ralconsistencywiththetime-seriesofpreviousobservations,
Et = argmax (Q,E,Et 1,v) (1)
aDBNcancorrectthecurrentoutputandreducetheproba- E✓Ot:C(E)BF �
bilityofacontexttakinganincorrectvalue. Apartfromthe
SinceourgoalistohavehighcertaintyforquerysetQ,weconsider
temporalconsistency,amisclassificationcanbecorrectedus-
theinformationgainforquerysetQ(alsocalledVOI)astheutility
ingrelationshipswithothercontextsiftheobservationsare
functionwhichisgivenby:
availableforthem.
(Q,E,E ,v)=H(QE =v) H(QE,E =v)
2. HindSightThepreviouscasedescribedusinghistorytocor- F t�1 | t�1 � | t�1
(2)
rectoutputatthecurrenttime.Similarly,aDBNcanbeused
whereH(.)istheentropyfunction. Wemustnotethatcomputing
toimprovetheconfidencesinhistoricalobservations,which
optimal-setofclassifierstobeexecutedatthenextiterationisan
perhapshadlowconfidence. Futureobservationswithhigh
NP-hardproblem[10]. Wesolve thisoptimizationproblem using
confidencecanbeusedtoimprovetheconfidenceofprevious
a greedy approach as described in Algorithm 1. The results by
observations. Thiscapabilitycanbeexploitedwhenqueries
Nemhauser et al. [16] and Krause et al. [10] have shown that a
specifyahigherdelaytolerancethreshold.
greedyalgorithmprovidesasolutionwithintheconstantfactor(1
3. ValueofInformationTheDBNcanalsobeusedtoassess 1)oftheoptimalsolutionforageneralbayesiannetwork. �
e
the value of a context-inference algorithm. Value of infor- Our extension of optimization problem to the DBN does not
mation(VOI)isdefinedastheexpectedgainincertaintyfor changetheresultsaslongasweselectnodesEtfromasetSsuch
therandomvariablesinDBNifanadditionalobservationis thatthenodescorrespondingtoSinDBNareindependenttoeach
made. Thisisusefulforthequeryprocessor,whichcande- othergiventhequerysetQ. InaDBN,weselectEtfromasetof
cidewhatcontextobservationcanprovidehighestutility. observationnodesOtwhereeachobservationnodeisconnectedto
theDBNonlythroughaparenthiddennode. Hence,anypathbe-
4.2 QueryProcessor
tweenanytwoobservationnodesisalwaysblockedbyoneofthe
Thequeryprocessorisresponsiblefori)multi-queryoptimiza- parenthiddennodes.Thus,anytwoobservationnodesinDBNare
tioni.e. achievingtheconfidencerequirementsofcontextqueries d-separated[21]andhence,independentofeachother.
while operating within query constraints, ii) execution of privacy WenotethatAlgorithm1givesnear-optimalsolutionwheneach
leakagequeries,andiii)enforcementofuser-definedprivacypoli- classifierisassignedaunitcost. Inaspecificscenario,theremay
cies.Multi-queryoptimizationinCQueraisesfollowingchallenges: be several considerations in determining the cost, which depends
onthecostofsensing,processing,orobtaininghumaninput.Ifthe metricbetweenQandC0andisgivenasfollows:
resultingcostsarenon-uniforminnature,wereferthereaderstoan
algorithmasdescribedin[10]. D(QC0)=1 H(Q)�H(Q|C0)
| � H(Q)
Algorithm1ComputeOptimalSet Thismetrictakesvalue1ifQisindependentofC0andtakesvalue
0ifQisfullydeterminedbyC0.Weusethismeasureasaprivacy
Input:Budgetk;querysetQ;setOtfromDBN; scorewhereahigherscoreindicateslessinformationleakage.
utilityfunction ;previousobservedsetEt 1andcorrespondingsetof
classifieroutputFsv;CostfunctionC. � Forthesecondquerytype,auserisinterestedinidentifyingor-
Output:SetofobservationsE Ot dered list of contexts that reveal most information about queried
✓
LetE=� contextQ. ForeachcontextcsupportedinCQue,weuseprivacy
fori=1tokdo score D(Qc) as described above to measure information leaked
oE⇤==Ear[g{moa⇤x}o2Ot\EF(Q,E[{o},Et�1,v) aboutquerie|dcontextQ.Werankcontextsbasedonthismetric.
endfor Howtoenforceprivacypolicies?
return E
As described in §2, one of the benefits of CQue is that the DBN
providesin-depthandreal-timeinformationaboutcorrelationsbe-
tweendifferentcontextoutputsincontrastwithHMMsandother
Howtohandledelayrequirementsofqueries? techniquesthathavebeenleveragedinpriorwork.Ourfocusisnot
onidentifyingthebestprivacyexposurepolicyorprovingitspri-
InCQue, weunrolltheDBNsuchthatitcanaccommodatease-
vacyproperties;ratheritistodemonstratethatCQuecanbeused
riesofobservationsoflengthW. Ateachtime-step,weslidethe
todevelopsuchmethods.
DBNwindowoverobservationssuchthattheoldestobservations
To demonstrate these benefits, CQue supports a privacy policy
aredroppedandthelatestobservationsareaddedtotheDBN.As
thatcansuppresscontextvaluesthatleadstochangeinconfidence
a result of this sliding window, any context observation resides
ortheprobabilisticbeliefaboutaprivatecontextgreaterthanthresh-
in DBN for exactly W time-steps. Hence, we can compute the
old�.Suchapolicycanprovidegreaterprivacycontrolthanasim-
probabilityforqueriedcontextsW times,correspondingtohaving
pler policy that just blocks a specific private context from being
0,1,2,...,W 1 observations from the future. This allows our
� revealed.InCQue,ausercandefinethesepoliciesspecifictoapo-
frameworktoansweraquerywithadelayuptoW 1time-steps.
� tentialadversarialentitywherethisentitycanbeanapplicationor
Our framework can use this delay period in three cases: i) if the
agroupofapplicationsoralltheapplicationsontheuser’sphone.
sequenceofhistoricalobservationshavelowconfidenceresulting
Apolicyforagroupofapplicationscanbeusefultoprotectagainst
inlowconfidenceforthelatestobservation,ii)ifthesequenceof
informationcollusionamongtheseapplicationsinagroup.
historicalobservationshavefluctuatingvaluesforthecontextsindi-
In CQue, we assume a strong adversary having access to the
catinglowconfidenceintheoutputofcontextinferencealgorithms,
user’s DBN. Now, we enforce user policy by using the DBN to
andiii)ifthelatestobservationisdifferentfromthehistoricalob-
calculatetheconfidenceortheposteriorprobabilityfortheprivate
servations.Inthefirsttwocases,wewaitforthefutureobservations
context using the output for the set of contexts, requested by the
andhopethattheseobservationsareconsistentandhavehighcon-
adversarialentity,asobservationsinDBN.Also,wecomputeprior
fidence.Ifthishappens,weboosttheprobabilityofqueriedcontext
probabilityfortheprivatecontextwithoutusinganyobservations
inhindsight.Inthethirdcasewherethelatestobservationisdiffer-
intheDBN.Ifthedifferencebetweenposteriorprobabilityandthe
entfromthehistoricalobservations,itcanhappeneitherbecauseof
prior probability is greater than �, then we suppress the value of
intermittentmisclassificationbytheclassifierorbecausethecon-
thecontextthatcausesthemaximumchangeinprobabilities.Ifthe
textvaluehasactuallychanged.Anyfutureobservationcanhelpin
changeinprobabilitiesisstillhighwiththeremainingcontextout-
distinguishingthesecasesandimprovethecertaintyinthecontext.
puts,werepeattheprocessofremovingcontextvaluethatcauses
maximumchangeuntilwereachthethresholdlimit�. Werelease
Howtoexecuteprivacyqueries?
theremainingcontextstotheadversarialentity.
Unlikequeriesthatrequestcurrentstateofanindividual, privacy
4.3 LearningtheGraphicalModel
querieslookforaggregateinformationregardingthemutualinfor-
mationsharedbetweenacontextandsensordataorothercontexts Now,wedescribethechallengesinvolvedinlearningtheDBN
availableinCQue.Inourframework,weprovidetwotypesofpri- modelforthephoneuser. Therearetwochallengesinlearningthe
vacyqueries:i)userspecifiesaquerycontext,Qandwantstoknow DBN:a)howtolearnstructurewithoutexecutingallsensorsand
informationrevealedaboutthequeriedcontextfromaspecifiedlist contextinferencealgorithmscontinuouslyonthephonesincethis
ofsensors(S)andcontexts(C);andii)userspecifiesaquerycon- consumessignificantenergy,andb)howtominimizeinterruptions
textQandwantstoknowrankedlistofcontextsalongwithapri- ofthephoneusertoobtaingroundtruthlabelsforlearning.
vacy score that indicates how much information is revealed by a Context relationship hints: The learning process can degrade
rankedcontextaboutthequeriedcontext. Thus,thesequerieshelp userexperienceduetotheenergycostofrunningseveralcontext
userunderstandhowmuchinformationcanberevealedaboutcer- inference algorithms concurrently on the phone. While it is pos-
taincontextfromothercontexts. Together,thesetwoquerytypes sible to randomly sample a few contexts at a time, this can slow
enableuserstomakeadecisionregardingtheirownprivacy. downthelearningprocessastherandomapproachmaynotalways
Forthefirstquerytype,givenauserspecifiedsensorlistS we samplerelatedcontextstogether.Toperformefficientsamplingand
identifycontextclassifiersCSthatcanexecutegivensensorsS.In facilitatefasterlearningofpersonalizedrelationships,wemaintain,
additiontoS,usercanprovideasetofcontextsC. Now,wewant foreachcontext,alistofcontextstobesampledtogetherthatmay
tohaveanuncertaintymeasureinqueriedcontextQifcontextsin possiblyhavesoftrelationships.Thesoftrelationshipsmayormay
C0 = C CS areobserved. Weuseanormalizedvariantofmu- not hold for individual users and it does not necessarily result in
[
tualinformationbasedoninformationtheorythatgivesadistance edgesinDBNconnectingsoftly-relatedcontexts.Asitmaynotbe
possibletoenvisionallthesoftrelationships,westillperformran- isbeneficialasitprovideshigherclassificationaccuracyandbet-
domsamplingandbiasthesamplingforcontextsthatseemtobe teruncertaintyestimates. Theuseofpersonalizedclassifiersalso
related. benefitstheDBN—withpersonalizedclassifiers,thecorrectDBN
Whiletheuseofsoftrelationshipscanaddresstheefficientsam- parameterscanbelearntusingfewerfuturehumaninputresulting
plingproblem,wecanspeedupthelearningprocessandparameter inshortertrainingperiod.CQueprovidesanAPItothedevelopers
estimationinDBNbytheuseofhintsthatcanbeprovidedbythe ofcontextclassifierstoreceivehuman-providedlabelsthatcanbe
context-inference algorithm developers. A context developer can leveragedforpersonalization.
includehintsforeachcontextthatidentifypositivecausesandneg-
ativecausesforit.Asanexample,drivingisadirectpositivecause 5. IMPLEMENTATION
foruser’slocationbeingonstreetwhereasuser’slocationbeingat
WenowdescribetheimplementationofCQueonAndroidsmart-
homeisanegativecauseforonstreet. Thesehintscanbedirectly
phonesrunningAndroidOSversion2.2orhigher.
utilizedinsettingtheparametersforrelationshipsthataretruefor
amajorityofusers. Inference Engine: The CQue inference engine is designed to
run in real-time on a mobile phone to avoid any delays incurred
Minimizinghuman-providedlabels: Oneofthechallengesin
in accessing the cloud. The query processor maintains a model
learningthedynamicbayesiannetworkisthatwedonothaveac-
of a DBN that is personalized to the phone user. This model is
cess to ground truth for contexts. While ground truth can be ob-
storedasafileinanXMLBIFformatwhichisaninterchangefor-
tainedbyhavingtheuserprovidegroundtruthforaninitialtraining
matthatisrecognizedbymostbayesianinferencesoftwares.Inour
period,wewishtominimizeinterruptionsandhenceusethisoption
implementation,weusethewellknownvariable-eliminationalgo-
sparingly.Instead,wecansamplethecontextsinferredbythealgo-
rithm’simplementationforprobabilisticinferenceprovidedbythe
rithmsalongwiththeconfidencevalueassociatedwiththeoutput.
JavaBayespackage[1]. Onedownsideisthataccurateinference
Theinferredcontextvaluesprovideuswiththepartialobservations
usingvariable-eliminationcantakeexponentialtimeinthenumber
oftheunderlyingMarkovianprocess.WeusetheStructuralEMal-
ofvariablesinthebayesiannetwork. Whilethiswasnotanissue
gorithmdescribedin[7]tolearnthestructureoftheDBNfromthe
forthenumberofcontextsinourcurrentimplementation,thismay
partialobservations. Inthisalgorithm,thestructureoftheDBNis
beabottleneckasthenumberofcontextsincrease. Futureimple-
improvediterativelyuntiltheMDLscoreofthestructureB given
mentationsofCQuewillusefastapproximateinferencealgorithms
atrainingdatasetDconverges. LetussupposeastructureBcon-
such as importance sampling and MCMC simulation to optimize
sistingofnrandomvariablesX1,...,Xn. Weusethenotationxi inferenceperformanceonthephone[21].
and⇧xi todenoteanassignmentforXianditsparentsetPa(Xi) QueryPlan: Asdiscussedin§4.2,CQueusesadynamicquery
respectively. Also,N(xi,⇧xi)denotesnumberofinstancesinthe planwherethedecisionregardingwhatcontextstoexecutegivena
trainingdataDwhereXi =xiandPa(Xi)=⇧xi.LetN bethe certainenergybudgetisperiodicallyre-visitedtocapturedynamics
totalnumberofinstancesintrainingdata.Then,theMDLscorefor
intheincomingcontextstream. Suchdynamicadaptationcanbe
aDBNstructureBgivendatasetDisgivenas:
expensivetoperformonthephonesinceitdependsonthenumber
of contexts in the DBN. So, for infrequent plan evaluation or if
E[N(xi,⇧xi)|D]log✓(xi,⇧xi)�log2N#(Xi,Pa(Xi) the size of the DBN is small, our implementation performs such
Xi ˘xiX,⇧xi ¯ planningonthephone, butotherwiseoffloadsthecomputationto
thecloud.
nwumhebreer✓o(fxpi,a⇧raxmie)te=rs nEe[eENd[(eNxdi(,xt⇧io)x|irDe)|]pDre]saenndt P#r((XXii,PPaa((XXii)))i.sWthee DmBobNileLepahronnien,gl:earnWinhgiltehethDeBeNxecisutmioonreencgoimnepucatantioexneaclluyteinotnena-
needtotakeexpectedcountsaswehaveaccesstoo|nlypartialob- siveandrequirescloudsupport. WeimplementedtheDBNlearn-
servations. Wesimplifythelearningprocessbyfirstlearningthe ingalgorithmusingpartialobservationsbymodifyingtheWEKA
staticnetworkoftheDBNfollowedbylearningthetransitionedges package[9].CQueoffloadstheprocessoflearningtheDBNtothe
ofthenetwork.Thisprocessissuboptimalinnaturebutithasbeen cloudbysendingtheappropriatecontextinstancestotheserverat
showninpracticetoyieldparametersestimatesclosetooptimal. theendofeachday. TheDBNislearntattheendofeverydayin
WhilelearningtheDBNwithoutanyhumaninputisideal,this thecloudandissentbacktotheCQueframeworkinXMLBIFfor-
canleadtoerrorsparticularlyifthetrainingandtestcontextdistri- mat. Overtime,thefrequencyofupdatingtheDBNreducesasits
butionsareverydifferent. Thus,thereisaneedforatleastsome structureandparametersstabilize.Also,tofacilitatefasterlearning
corrective human input. We randomly sample human input for a ofDBNparameters,wemaintainaconfigurationfilethatprovides
smallfractionofcontextsin-ordertocorrectDBNparameters,and initialparameterestimatesfortheDBN,basedonaveragenumbers
assign these human-provided contexts higher weight over the in- from a general population. The configuration file has entries for
stancesobtainedusingcontext-inferencealgorithms. each context containing a list of positive and negative causes for
it along with the conditional probability. For example, the nega-
4.4 ClassifierPersonalization tivecauselistforthecontextatofficelookslike: athome,0.99;at
So far, our discussion has assumed that the underlying context store,0.85;atrestaurant,0.93;driving,1.0.
classifiersareblack-boxesi.e.theydonotexposetheirinternalbe- QueryProcessor: Weimplementedthecontextengineasaback-
haviororallowchanges. Thus,theDBNislimitedtothecontext groundservicerunningonandroidphones. Thisserviceisrespon-
outputanduncertaintyprovidedbytheclassifiers. Anaturalques- sible for activating context-inference algorithms and appropriate
tioniswhetherwecandobetterifwewereableprovidefeedback sensors, and providing the context values generated by the algo-
fromtheDBNtotheclassifierin-ordertoimproveitsperformance rithmstothequeryprocessor. Communicationbetweenthequery
further. processorandthecontextengineusesAndroidIPC.Ourimplemen-
In CQue, any human input provided to the DBN can not only tationcurrentlysupportssemanticlocationcontexts,socialcontexts
beusedtocorrectDBNparameters,butalsobeusedtopersonal- andactivitycontexts: a)Activitycontexts: walking, drivingand
izetheclassifiers. Aclassifierpersonalizedforanindividualuser stationary, b) Social contexts: with friends, with colleagues and
alone,andc)Locationcontexts: atrestaurant,athome,atoffice, ActivityDataset: Whiletherealityminingdatasethasrichmulti-
atstoreandonstreet. sensordata,itreliesonself-reportsforactivityclassification,hence
Ourimplementationusesdecision-treeclassifiersforactivityrecog- itdoesnotallowustounderstandhowaccuracyofactivityclassi-
nition. Weusedwell-knownsetoffeaturesextractedfromthe3- fierscanbeimprovedthroughtheuseofCQue.Toaddressthis,we
axisaccelerometerandgyroscopereadings.Thefollowingfeatures collectedatraceofusercontextsfortwoweeksfromsevenusers.
werecomputedforaccelerometerreadingsalongeachofthethree Eachofthesevenusersprovidedabout50contextlabelsperday.
axis: mean, standard deviation, mean-crossing rate, energy given Sincetherecouldbelabelingerror,wecorrectedactivitycontextin-
asnormalizedsumofsquareddiscreteFFTcomponent,peakfre- formationusingGPSinformationandmanualcorrectionattheend
quency in FFT and peak energy. In addition, we computed cor- ofeachday. Allusersprovidingdataweregraduatestudents. We
relationbetweeneachpairofaxes. Thegyroscopereadingswere useddatafromfouruserstotraintheactivityclassifiers. Fortest-
usedtocomputemeanandstandarddeviationofangularvelocity ing,weusedtheseclassifierstoclassifyactivitiesfortheremaining
aroundeachofthethreeaxis. Forclassifyingsocialandlocation threeusers. Foreachuserinthetestset,wedividedtheirdatainto
contexts,weusedauser-providedmappingfrombluetoothdevices twoweeks—inthefirstweek,apersonalizeduser-specificDBN
to the social context and a mapping from WiFi access points to istrainedusingtheoutputsoftheclassifieralgorithms,andafew
thecorrespondingsemanticlocation(eitheruserprovidedorusing humanlabelsobtainedfromtheground-truthset(dependingonthe
publiclyavailabledatabases). Unlikeactivitycontexts,theclassi- interruptionlimits,andlabelselectionscheme),andthedatainthe
fiedsocialandlocationcontextshavehigherconfidenceassociated secondweekisusedtoevaluateCQue.
withthem.Whilewerestrictedourselvestotheabovecontextsdue
tothenatureofourdatasets,ourarchitectureisgeneralisdesigned SetofContexts.
toaccommodateothercontext-enginesthatmaybeaddedbyprac- Inboththedatasets,wehaveasetofcontextsthatincludeslo-
titioners. cation contexts like at home, at office, on street and three activ-
ity contexts, namely, walking, driving and stationary. For social
UserInterface: TheCQueframeworkprovidesaninterfaceto
contexts,weusethegroupsofbluetoothdevicesinthecloseprox-
theusertospecifyanenergybudgetandpreferencesforinterrup-
imityofauserasthesocialcontext. InActivitydataset,wehave
tion(duringtraining)andprivacy.Theenergybudgetcanbespeci-
thesegroupsexplicitlylabeledasfriends,colleagues,roommates,
fiedasafractionofafullbatterythatmaybeusedforrunningCQue
spousewhereasinRealityminingdataset,wedonothaveexplicit
andabatterythresholdbelowwhichCQueshouldbestopped. For
labelsbutweidentifythesegroupsasgroup-1,group-2andsoon.
interruptionpreferences,ausercanspecifythetotalinterruptsper-
Foreachcontext,thecorrespondingclassifieroutputstrueorfalse
mittedperdaywhiletrainingtheDBN.Forprivacypreferences,a
valuealongwiththeconfidencevalueassociatedwiththeclassifi-
usercanselectprivatecontexts,selectalevelofprotectionagainst
cationoutput.WeusethesevaluesasinputinDBN.
adversaryfromlow,mediumandhighwherethesevaluesmapto
valuesforthresholdforchangeinadversarialconfidence� = 0.4,
� = 0.25and� = 0.05respectively. Additionally,usercanselect Evaluationmetrics.
theapplicationsforwhichthesepreferencesapply. We use three performance metrics in our evaluation: 1) Accu-
racy,whichiscomputedasthefractionofthesecontextsthatare
correctlyclassified,2)Confidence,ortheprobabilityofthemost
6. EXPERIMENTALRESULTS likelyvalueforthecontextprovidedbytheDBN,and3)F-Measure
which gives the harmonic mean of recall and precision (higher
Inthissection,wedescribethesetofexperimentsperformedto
scoreisbetter)
evaluate our framework. We first describe the data sets and the
evaluationmetricsusedfortheexperimentalevaluation. Next,we
6.2 Cost-AccuracyTradeoffsusingDBN
presentouranalysisofprivacyforvariouscontexts.Wethenlookat
theenergy-accuracytradeoffsanddemonstratethebenefitsofusing OneofthebenefitsofaDBNisthatitallowsatradeoffcostfor
DBNovercontext-classifiersusingasetofexperiments,andcon- accuracy — by understanding the relations across different con-
cludewithanevaluationofhowvariousparameterssuchasdelay, texts,aninferencealgorithmcanbeturnedofftoreduceoverhead.
andinterruptionbudgetimpactresults. While the “cost" can be different depending on the sensing and
communicationneedsoftheinferencealgorithm,ortheburdenof
6.1 DataSetsandEvaluationMetrics user input, we use a simplistic model where we assume that all
classifiershaveequalcost.Thismodelprovidesanintuitiveunder-
standingofthecost-accuracytradeoffs.
Datasets.
Inordertoconductourexperiments,weusedfollowingtwodatasets:
Classifiers<Queries. Wefirstlookatthecasewherethesys-
Reality Mining Dataset: This data set contains data collected temrunsfewerclassifiersthanthenumberofqueriesandexploits
continuouslyfor100studentsandstaffatMIToveracademicyear thecontext-relationshipstoanswercontextqueriesforwhichthere
2004-2005. This data provides various contexts like user’s loca- arenoobservationscomingfromtheclassifier. Thiscasedemon-
tion(work, home, other)basedoncell-towerobservations, social stratesthattheDBNcanbeusedtoanswerthecontextqueriesin
contextsbasedonproximityofbluetoothdevicesandphysicalac- expectationwhichisnotpossibleotherwiseusingclassifiersalone.
tivities like stationary, walking and driving based on self-reports. WeevaluatethismodelusingtheRealityMiningdataset.
The duration of user traces varied from 30 days to 269 days. In Inthisexperiment,weconsiderallthecontextsthatwesupport
our evaluation, we used data from 37 users who had data for at to be in the query set. We then vary the budget such that it can
least10weeks. Thetracesforeachoftheseuserscontains3loca- executebetween1to5classifiers. Basedonthebudget,thesetof
tioncontextsand3activitycontexts. Inaddition,wederivesocial classifiersarechosenthatcanprovidemaximuminformationgain
contextsusingclusteringofbluetoothdevicesthatappearcloserin asdescribedin§4.2.
time, thusthenumberofsocialcontextsvariedfromusertouser Figure 4 shows the accuracy and F-measure averaged over 37
andtherewereatleast2suchcontexts. usersasthenumberofclassifiersincreases.Weseethatboththese
1 1
0.9 0.9
0.8
0.8
0.7
0.7 cy 0.6
a
0.6 ur 0.5
cc 0.4
0.5 A 0.3
0.4 0.2
0.1
0.3
0
0.2 Accuracy(Static) U1U2U3 U1U2U3 U1U2U3
0.1 AFcc-Mureaacsyu(Drey(nSatamtiicc)) Walk Stationary Drive
F-Measure(Dynamic) -
0
1 2 3 4 5 Classifier
Number of Classifiers DBN-Gain
(a)
Figure 4: Effect of varying number of executing context classifiers
whennumberofqueries( 6)ishigherthanthenumberofexecuting 1
� 0.9
contexts. ThefigureshowsaggregateaccuracyandF-measureover37
0.8
usersforstaticanddynamicplanexecution. 0.7
e
ur 0.6
s
a 0.5
e
M 0.4
metricsimproveasweincreasethenumberofclassifiers,andeven F- 0.3
withtwoclassifiersexecuting,wegetfairlygoodaccuracyandF- 0.2
measure. Thus, significant benefits can be obtained with only a 0.1
smallnumberofclassifiersbyleveragingaDBNmodeloftherela- 0
U1U2U3 U1U2U3 U1U2U3
tionsacrosscontexts.
Walk Stationary Drive
Itmightseemsurprisingthatveryhighaccuracycanbeachieved -
evenwithasingleclassifier. Infact,thisisbecausemostcontexts Classifier
havebiaseddistribution(e.g. thestationarystateistrue(> 90%) DBN-Gain
ofthetime),hencehighaccuracycanbeachievedbyjustusingthe (b)
modelinexpectationwithnoclassifiersexecuting! Butthisgives
poorrecall,precisionandconsequentlytheF-measureislow,and Figure5: AccuracyandF-Measureforvariousactivityclasses
morecontextinputisrequiredtoimprovethesemetrics. Figure4 forClassifieronlyandgainprovidedbyClassifier+DBNmech-
showsthatDBNachieves75%F-measureand94%accuracyusing anismondatafrom3users.ResultsshowthatusingtheDBNis
only4classifiersresultinginatleast33%costreduction. beneficialinmostcases,exceptinthecaseofrarelyoccurring
Figure 4 also compares the use of a dynamic plan where the contexts.
queryplanisre-evaluatedevery20minutesvsastaticplanwhere
thequeryplanisevaluatedonce.Thedynamicplanprovidesbetter
accuracyandF-measurethanstaticplanacrosstheboard. Thisis correcttheoutputsprovidedbytheclassifiersandhence,improve
becausethedynamicplanselectsappropriatesetofclassifiersusing recall and precision. Since the DBN performs corrections using
valueofinformationprovidedbytheobservedclassifieroutputsin thetemporalconsistency,therecallvalueforacertaincontextmay
real-time whereas static plan selects the set of classifiers without dropifthecontextisrarelyseenandlastsforaveryshortduration.
consideringthereal-timeclassifieroutputs. Inourtraces,weobservedthatthewalkingactivitywasinfrequent.
Insuchacase,iftheclassifierdoesnotgenerateoutputwithhigh
Classifiers>Queries. Wenowlookatthebenefitsofusinga confidencetheDBNcansmoothoutthewalkingcontextresulting
DBNwhenthenumberofclassifersthatexecutearegreaterthanthe inreducedrecallbuthigherprecision. Asaresult,F-measuredoes
numberofqueries. WeusetheActivitydatasetinthisstudy,since not see significant gain. In contrast, the driving context though
we have raw data for activity inferences, which are typically the rarelyseenascomparedtostationarylastslongerandhence,sees
contextswithhighestinaccuracy. Welookattheperformancefor improvementinrecallandhence,improvesF-measure.
thethreeactivitycontexts(walking,drivingandstationary),which
6.3 EvaluatingPrivacy
havehighestuncertainty. Wecomparetwomechanisms: a)using
justtheactivityrecognitionclassifiers,andb)usingallthecontext In this section, we evaluate two aspects of how CQue can be
classifierswithDBN.Weshowresultsforthreerepresentativeusers usefulindealingwithprivacybreachesduetocorrelationsacross
inthedataset. contexts.
Figure5(a)showsthataccuracyisworstwhenweuseonlyclas- Location-SocialRelations: Inourfirstexperiment,welookata
sifiersalone,andtheuseofDBNimprovesimproveaccuracysig- twosensorscenarioandunderstandthecorrelationsbetweenthese
nificantly. TheimprovementinaccuracybyusingDBNforusers contextsacrossdifferentdatasets. Recallthatlocationisobtained
U1andU2isabove24%forbothstationaryanddrivingcontexts. through GPS and social interactions through bluetooth. We use
Improvementsaresmallforwalkingactivity(2%)sinceDBNnei- CQuetounderstandhowaprivacybreachcanoccurforaprivacy-
ther observed any context strongly related with walking nor the sensitivecontextthroughindirectobservations. Here, weassume
temporal consistency. This was because walking was a rare con- astrongadversarythathasaccesstothepersonalizedDBNfora
textandrarelylastedforlongerthanafewminutes. user. Potentially, this is possible if various apps on a phone that
Figure5(b)showsF-measureforvariousclasses. Weseethat observedifferentcontextsdecidetocolludeandcombinetheirdata
thismetricgenerallyimprovessincethemainroleoftheDBNisto togenerateacompleteDBN.
(a) PrivacyleakageinAllLocationContexts
1
Social | Location-RM
0.9 Location | Social-RM Observation
Social | Location-AD Metric None Allsocial
0.8 Location | Social-AD
contexts
0.7
Accuracy(RM) 73.9 3.0 74.81 2.73
± ±
0.6 Accuracy(AD) 61.11 5.55 88.9 6.26
F ± ±
D 0.5
C (b) PrivacyleakageinAllSocialContexts
0.4
Observation
0.3
Metric None Alllocation
0.2 contexts
0.1 Accuracy(RM) 73.86 2.9 74.18 2.82
± ±
0 Accuracy(AD) 64.01 15.29 87.3 5.94
0 0.2 0.4 0.6 0.8 1 ± ±
Privacy Score
Table 1: Cross-context privacy leakage in location and so-
Figure6: Cumulativedistributionofprivacyscoresforcross- cialcontextsfori)RealityMiningdataset(RM),andii)Activity
contextpairsini)RealityMiningdataset(RM),andii)Activity dataset(AD).Foractivitydataset,weseesignificantincreasein
dataset(AD). From the distribution of privacy scores, we can accuracy for all the location contexts when all the social con-
concludethatthereissignificantcross-contextprivacyleakage textsareobservedandviceversa. Forrealityminingdataset,
inActivitydatasetwhereasnosuchleakageisobservedinRe- accuraciesdonotchangewithcross-contextobservations.
alityMiningdataset. Thisemphasizestheimportanceofper-
sonalizedDBNtoevaluateprivacyforauser. 1
0.8
Givensuchanadversary,aprivacy-sensitivecontextcanbein- d
e
ferredbyobservingoneormoreothercontexts. Usingourprivacy as 0.6
e
score,wecanrankcontextsinthedecreasingorderofitscapabil- el
R
itytoinferprivacy-sensitivecontext. Inthisexperiment,welook a 0.4
at
twoscenarios: a)allcaseswhereweuseonelocationcontextasa D
privatecontext(p)andasocialcontextoasobservation,andb)the 0.2
reversescenariowhereasocialcontextareprivateandlocationis
observed. We evaluate privacy score D(po) for each pair p,o 0
ofcontexts. Figure6givesdistributionofp|rivacyscoresforhthesei 0.4 0.2 0.1 0.05
scenariosfortheRealityMiningandActivityDatasets. δ
Theresultsareinteresting—weseethatthereisconsiderably
highercorrelationacrosslocationandsocialcontextinthecaseof Figure 7: Fraction of data that can be released to prevent
the activity dataset than in the reality mining data. This is also change in adversarial confidence greater than � for a private
reflected in Table 1, which shows the accuracy of inferring loca- contextathome.
tion/social only based on the prior distribution of these contexts
v.s.observingtheothercontext.Theresultsshowthatthereisonly
releasedasaresultofthissuppressionforvariousvaluesof�where
asmallchangeinthecaseoftheRMdataset,buttheaccuracyin-
wechoseathomeastheprivatecontextandrestofthecontextsas
creasesbymorethan25%inthecaseoftheADdataset. Inother
queries.Asmallervalueof�resultsinmuchtighterprivacycontrol
words,anadversarywouldbeabletoinferanindividual’slocation
butreleasestoofewcontexts.
withsubstantialaccuracyiftheyonlyhadaccesstothebluetooth
Table2showsthefractionofdatasuppressedincontextofeach
informationontheADdataset. Ourexplanationfortheseresults
category: social,location,andactivityforvalueofthreshold�
isthatbluetoothusageisfarmoreprevalentinrecenttimes,there- 2
0.1,0.4 . In this case, we choose at friend’s place as a private
forethecorrelationshaveincreased. Overall,ourresultsshowthat { }
context available in Activity dataset. We can see that CQue sup-
CQuecanbeusedtoprovideintuitionaboutthecorrelationsacross
presses in an intelligent manner where it suppresses highly cor-
contexts, therebyenablingmoreinformeddecisionaboutwhatto
relatedsocialcontextmorefrequentlythanthelesscorrelatedlo-
expose.
cation or activity contexts. If we were to use a mechanism like
SuppressionPolicyforPrivacy: Inoursecondexperiment,we MaskIT [8], it would suppress every context at the same level.
lookathowCQuecanbeusedtoimplementareal-timesuppression SinceCQuereleasesmorecontexts, itresultsinhigherutilityfor
policy for protecting privacy. Our policy is intended to be repre- theapplicationsandtheuserswhousetheseapplications.
sentativeandillustratehowtheDBNmaybeused,andwemakeno Inconjunction,theseexperimentsdemonstratethepotentialuse
formalclaimsregardingitsprivacyproperties. Inourexperiment, ofCQuebothforunderstandingprivacyimplicationsofreleasing
theusercandefineapolicythatpermitsreleasingamaximalsetof a context, as well as to implement privacy policies that leverage
contextobservationssuchthatthechangeinadversarialconfidence relationsacrosscontexts.
forprivatecontextuponobservingthissetislessthanthreshold�.
6.4 DBNwithPersonalizedClassifiers
Asaresult,someofthenon-privatecontextscanbesuppressedin
real-timetocontrolthechangeinadversarialconfidence. Thus,it While all our previous results have assumed classifiers to be
resultsinlowerutilityfortheapplicationsseekingcontexts.Figure black-boxcodethatcannotbemodified, wenowlookatthecase
7givesanexampleshowingthefractionofcontextdatathatcanbe wherewecanpersonalizetheclassifiersusingthehumaninputob-