1. Center for E-Business Technology Seoul National University Seoul, Korea Private Queries in Location Based Services: Anonymizers are not Necessary Gabriel Ghinita 1, Panos Kalins 1, Ali Khoshgozaran 2, Cyrus Shahabi 2, Kian-Lee Tan 1 1 Dept. Of Computer Science, National University of Singapore 2 Dept. of Computer Science, University of Southern California SIGMOD 2008 2009. 02. 05. Summarized and Presented by Babar Tareen, IDS Lab., Seoul National University Based on original conference slides

2. Copyright  2008 by CEBT Introduction  LBS Queries can disclose Health conditions Lifestyle habits Political affiliations Religious affiliations  Privacy is not protected by using a fake identity  Location Server is not trusted 2 “Find nearest hospital to my pr esent location”

3. Copyright  2008 by CEBT K-anonymity  Query issuer “hides” among other K-1 users Probability of identifying query source ≤ 1/K Idea: anonymizing spatial regions (ASR) 3

4. Copyright  2008 by CEBT Drawbacks  The anonymizer is a single point of attack  CR can only be constructed if large number of users have subscribed  It is assumed that attacker has no background information Alice queries for women’s clinic and CR contains Alice and Bob  Privacy is guaranteed for static snapshot of user location Alice can easily be identified if she asks same query as she moves, because she will be present in all CRs 4

5. Copyright  2008 by CEBT Casper [Mok06] 5  Quad-tree based Fails to preserve anonymity for outliers Unnecessarily large ASR size u1u1 u2u2 u3u3 u4u4 A1A1 A2A2 u 4 ’s identity is disclosed If u 4 queries, ASR is A 2 If any of u 1, u 2, u 3 queries, ASR i s A 1 Let K=3

6. Copyright  2008 by CEBT Contribution  A novel framework for private location dependent queries based on PIR protocols (No need for any trusted third party)  Algorithms for approximate and exact nearest neighbor search  Reasonable computational cost 6

7. Copyright  2008 by CEBT 7 Private Information Retrieval (PIR) Protocol Computationally hard to find i from q(i) Bob can easily find X i from r

8. Copyright  2008 by CEBT Idea ! 8 Get Geographical Regions A, B, C, D Get Hospitals (POI) in Region A List of Hospitals Location Compromised GetHospitals in Region A using PIR

9. Copyright  2008 by CEBT Computational PIR Protocol 9

10. Copyright  2008 by CEBT Example 10

11. Copyright  2008 by CEBT 11 Approximate Nearest Neighbor Data organized as a square matrix  Each column corresponds to index leaf  An entire leaf is retrieved – the closest to the user p4p4 p6p6 p5p5 p8p8 p1p1 p2p2 p7p7 p9p9 p3p3 u

12. Copyright  2008 by CEBT 12 Z4Z3Z2Z1Z4Z3Z2Z1 Exact Nearest Neighbor QNR Only z 2 needed p4p4 p3p3 p2p2 p1p1 4 3 2 1 DCBA A3: p 1, p 2, p 3 A4: p 1, --, -- u Y 1 Y 2 Y 3 Y 4

13. Copyright  2008 by CEBT Optimization  Compression  Rectangular Matrix  Avoiding Redundant Multiplications  Parallelism 13

14. Copyright  2008 by CEBT Experimental Settings  Datasets Sequoia dataset: 62K POI Synthetic sets: 10K - 100K POI  Modulus up to 1280 bits  P4, 2.8GHz CPU 14

15. Copyright  2008 by CEBT Parallel Execution 15

16. Copyright  2008 by CEBT Re-using Partial Products 16

17. Copyright  2008 by CEBT Disclosed POI 17

18. Copyright  2008 by CEBT Conclusions  PIR-based LBS privacy No need to trust third-party Secure against any location-based attack  Downside Can be computationally intense for small devices. 18