A Framework for Protec/ng Worker Loca/on Privacy in Spa/al Crowdsourcing

Transcription

1 A Framework for Protec/ng Worker Loca/on Privacy in Spa/al Crowdsourcing Nov Hien To, Gabriel Ghinita, Cyrus Shahabi VLDB

2 Mo/va/on Ubiquity of mobile users 6.5 billion mobile subscrip/ons, 93.5% of the world popula/on [1] Technology advances on mobiles Smartphone's sensors. e.g., video cameras Network bandwidth improvements From 2.5G (up to 384Kbps) to 3G (up to 14.7Mbps) and recently 4G (up to 100 Mbps) VLDB [1] hop://mobithinking.com/mobile- marke/ng- tools/latest- mobile- stats/

3 Spa/al Crowdsourcing q Crowdsourcing Outsourcing a set of tasks to a set of workers q Spa/al Crowdsourcing Crowdsourcing a set of spa%al tasks to a set of workers. Spa%al task is related to a loca/on.e.g., taking pictures Loca/on privacy is one of the major impediments that may hinder workers from par/cipa/on in SC VLDB

4 Problem Statement Current solu/ons require the workers to disclose their loca/ons to untrustworthy en//es, i.e., SC- server. Requesters SC- server Report loca+ons Workers A framework for protec/ng privacy of worker loca/ons, whereby the SC- server only has access to data sani/zed according to differen%al privacy. VLDB

5 Outline v Background v Privacy Framework v Worker PSD (Private Spa/al Decomposi/on) v Task Assignment v Experiments VLDB

6 U/lity- Privacy Trade- off 100% Utility 0% 100% Privacy 0% VLDB

7 Related Work v Pseudonymity (using fake iden/ty) e.g. fake iden/ty + loca/on == resident of the home v K- anonymity model (not dis/nguish among other k records) iden//es are known the loca/on k- anonymity fails to prevent the loca/on of a subject being not iden/fiable all k users reside in the exact same loca/on k- anonymity, do not provide rigorous privacy v Cryptography such technique is computa%onal expensive =>not suitable for SC applica/ons VLDB

8 ε 1 Differen/al Privacy (DP) DP ensures an adversary do not know from the sani/zed data whether an individual is present or not in the original data ε - dis$nguishability [Dwork 06] A database produces transcript U on a set of queries. Transcript U sa/sfies ε - dis/nguishability if for every pair of sibling datasets D 1 and D, D and 2 1 = D2 they differ in only one record, it holds that : privacy budget Pr[ QS ln Pr[ QS DP allows only aggregate queries, e.g., count, sum. L - sensi+vity: D D2 Given neighboring datasets 1 and, the sensi/vity of query set QS is the the maximum change in their query results q σ ( QS) = max QS( D ) QS( D ) D, D 1 2 i= 1 [Dwork 06] shows that it is sufficient to achieve Laplace noise with mean D D 1 2 = = U] U] ε ε 1 λ = σ (QS) / ε VLDB DP by adding random

9 Outline v Background v Privacy Framework v Worker Private Spa/al Decomposi/on v Task Assignment v Experiments VLDB

10 Privacy Framework 0. Workers send their loca/ons to a trusted CSP SC-Server Cell Service Provider 1. CSP releases a PSD according to ε. PSD is accessed by SC- server 2. SC- server receives tasks from requesters PSD 2. Task Request t 1. Sanitized Release 3. Geocast {t,gr} Worker Database 0. Report Locations 3. When SC- server receives task t, it queries the PSD to determine a GR that enclose sufficient workers. Then, SC- server ini/alizes geocast communica/on to disseminate t to all workers within GR 4. Workers confirm their availability to perform the assigned task Requesters 4. Consent Workers trust SCP GR Workers do not trust SC- server and requesters Focus on private task assignment rather than post assignment Workers VLDB

11 Design Goal and Performance Metrics Protec/ng worker loca/on may reduce the effec/veness and efficiency of worker- task matching, captured by following metrics: Assignment Success Rate (ASR): measures the ra/o of tasks accepted by workers to the total number of task requests Worker Travel Distance (WTD): the average travel distance of all workers System Overhead: the average number of no/fied workers (ANW). ANW affects both communica%on overhead required to geocast task requests and the computa%on overhead of matching algorithm VLDB

12 Outline v Background v Privacy Framework v Worker PSD (Private Spa+al Decomposi+on) v Task Assignment v Experiments VLDB

13 Adap/ve Grid (Worker PSD) Creates a coarse- grained, fixed size 1 m 1 grid over data domain. Then issues count queries for each level- 1 cell using m 1 m 2 m1 1 ε 1 N ε = max 10, 4 k2 m m2 Par//ons each level- 1 cell into 2 m 2 level- 2 cells, is adap/vely chosen based on noisy count N' of level- 1 cell m 2 1 N' ε 2 = 4 k2 ε = ε 1 + ε 2 Level 1 Level 2 C A c1 c2 c c3 c c 15 4 c7 c8 c16 c17 c 18 c11 VLDB c c19 c20 c21 13 c9 c10 D c B ' ' ( N = 100) ( = 100) A N B ' ' ( N = 100) ( = 200) C N D [Qardaji 13] c5 c6

14 Customized AG 2 2 k / n = N' / m = Expected #workers (noisy count) in level- 2 cells 2 2 N'=100 m 2 large m n ε ε L Original AG ( k 2 = 5) leads to high communica+on cost n ε ε 2 m J Customized AG ( k 2 2, p = 88%) = h Increase to decrease overhead, but only to the point where there is at least one worker in a cell The probability that the real count is larger than zero: p h 1 count = PSD 1 exp 2 VLDB / ε 14 2 n ε

15 Customized AG Original AG and Customized AG adapts to data distribu/ons Original AG minimizes overall es/ma/on error of region queries while customized AG increases the number of 2 nd level cells Yelp Dataset Original AG Customized AG VLDB

16 Outline v Background v Privacy Framework v Worker PSD (Private Spa/al Decomposi/on) v Task Assignment v Experiments VLDB

17 Analy/cal U/lity Model We define Acceptance Rate as a decreasing func/on of task- worker distance (e.g. linear, Zipian) p a a = F( d); 0 p 1 SC- server establishes an Expected U%lity ( EU ) threshold, which is the a targeted success rate for a task. EU > p. X is a random variable for an event that a worker accepts a received task P( X a = True) = p ; P( X = False) = 1 p a Assuming w independent workers. U is the probability that at least one worker accepts the task X ~ Binomial ( w, p ) U = 1 (1 p a ) w a VLDB

18 Acceptance Rate Func/ons 0.5 Acceptacerate 0 distance MTD VLDB

19 Geocast Region Construc/on Determines a small region that contains sufficient workers Greedy Algorithm (GDY) 1. Init GR = {}, max- heap of candidates Q = { the cell that contains } 2. c i Q 4. If U EU, return GR 5. neighbors = { ci ' s neighbors} GR MTD Q Q 3. U (1 U )(1 ) 1 U c i = Q neighbors 6. ; Go to 2. t c1 c2 c c 3 4 c9 c10 c c t c5 c6 c7 c8 c 13 c 14 c16 c17 c 15 c 18 c19 c20 c21 VLDB

20 Par/al Cell Selec/on L The number of workers can s/ll be large with AG, especially when ε 2 small Allow par$al cell inclusion on the lastly added cell ci c i t 7 t 6 Sub-cell c i ' t 5 c1 c2 c3 c4 c5 c6 c7 c8 t 8 t 0 t1 2 t 4 t t3 Splitng c i c c 10 9 t c11 c12 c 13 c 14 c16 c17 Splitng 7 c 15 c 18 c19 c20 c21 c VLDB

21 Communica/on Cost Cellular Internet WLAN The more compact the GR, the lower the cost Digital Compactness Measurement [Kim 84] area( GR) DCM = area( MIN BALL) Measurement: Hop count Farthest distance between twoworkers = 2 Communicationrange c1 c2 c 3 Mobile Ad- hoc Networks Infrastructure- based Mode v.s Infrastructure- less Mode c 9 c 10 c 13 c 14 c 15 c16 c17 c c11 c VLDB c19 c20 c21 t c 4 c5 c6 c7 c8

22 Geocast Regions A B C D VLDB

23 Outline Background Privacy Framework Worker PSD (Private Spa/al Decomposi/on) Task Assignment Experiments VLDB

24 Experimental Setup Datasets Name #Tasks #Workers MTD (km) Gowalla 151,075 6, Yelp 15,583 70, Assump/ons Gowalla and Yelp users are workers Check- in points (i.e., of restaurants) are task loca/ons Parameter setngs ε = {0.1, 0.4, 0.7,1} 1000 random tasks x 10 seeds EU ={0.3,0.5,0.7,0.9} MaxAR = {0.1, 0.4, 0.7,1} VLDB

25 GR Construc/on Heuris/cs (Gow.- Linear) GDY = geocast (GREedy algorithm) + original Adap/ve grid (AG) [Qardaji 13] G- GR = geocast + AG with customized GRanularity G- PA = geocast with PAr/al cell selec/on + original Adap/ve grid (AG) G- GP = geocast with Par/al cell selec/on + AG with customized Granularity ANW WTD- FC HOP 0.5 GDY G-GR 0.4 G-PA G-GP GDY G-GR 8 G-PA G-GP GDY 6 G-PA 4 2 G-GR G-GP 0 Eps=0.1 Eps=0.4 Eps=0.7 Eps=1 0 Eps=0.1 Eps=0.4 Eps=0.7 Eps=1 0 Eps=0.1 Eps=0.4 Eps=0.7 Eps=1 VLDB

26 Effect of Grid Size to ASR Over-provision Under-provision 80 ASR Gowalla-Linear Gowalla-Zipf Yelp-Linear Yelp-Zipf k2 Average ASR over all values of budget by varying k2 VLDB

27 Compactness- based Heuris/cs (Yelp- Zipf) 10 HOP 80 ANW G-GP-Pure 2 G-GP-Hybrid G-GP-Compact 0 Eps=0.1 Eps=0.4 Eps=0.7 Eps= G-GP-Pure 20 G-GP-Hybrid G-GP-Compact 0 Eps=0.1 Eps=0.4 Eps=0.7 Eps=1 VLDB

28 Overhead of Archieving Privacy (Gow.- Zipf) 60 ANW WTD- FC ASR Privacy Non-Privacy 0 Eps=0.1 Eps=0.4 Eps=0.7 Eps= Privacy Non-Privacy 0 Eps=0.1 Eps=0.4 VLDB 2014 Eps=0.7 Eps= Privacy 20 Non-Privacy 0 Eps=0.1 Eps=0.4 Eps=0.7 Eps=1 28

29 Effect of Varying MAR (Yelp- Linear) ANW Eps=0.1 Eps=0.7 Eps=0.4 Eps= WTD- FC Eps=0.1 Eps=0.7 Eps=0.4 Eps=1 8 6 CELL Eps=0.1 Eps=0.7 Eps=0.4 Eps= AR=0.1 AR=0.4 AR=0.7 AR=1 0 AR=0.1 AR=0.4 AR=0.7 AR=1 0 AR=0.1 AR=0.4 AR=0.7 AR=1 VLDB

30 Effect of Varying EU (Yelp- Linear) ANW Eps=0.1 Eps=0.4 Eps=0.7 Eps=1 0 EU=30 EU=50 EU=70 EU=90 WTD- FC Eps=0.1 Eps=0.4 Eps=0.7 Eps=1 0 VLDB 2014 EU=30 EU=50 EU=70 EU=90 CELL 8 Eps=0.1 Eps=0.4 6 Eps=0.7 Eps= EU=30 EU=50 EU=70 EU=90 30

31 Demo hop://geocast.azurewebsites.net/geocast/ VLDB hops://

32 Conclusion Introduced a novel privacy- aware framework in SC, which enables workers par/cipa/on without compromising their loca/on privacy Iden/fied geocas/ng as a needed step to preseve privacy prior to workers consen/ng to a task Provided heuris/cs and op/miza/ons for determining effec/ve geocast regions that achieve high assignment success rate with low overhead Experimental results on real datasets shows that the proposed techniques are effec/ve and the cost of privacy is prac/cal VLDB

33 References Hien To, Gabriel Ghinita, Cyrus Shahabi. A Framework for Protec%ng Worker Loca%on Privacy in Spa%al Crowdsourcing. In Proceedings of the 40th Interna/onal Conference on Very Large Data Bases (VLDB 2014) Hien To, Gabriel Ghinita, Cyrus Shahabi. PriGeoCrowd: A Toolbox for Private Spa%al Crowdsourcing. (demo) In Proceedings of the 31st IEEE Interna/onal Conference on Data Engineering (ICDE 2015) VLDB