1.  A Two-level Protocol to Answer Private Location-based Queries Roopa Vishwanathan Yan Huang [RoopaVishwanathan, huangyan]@unt.edu Computer Science and Engineering University of North Texas

2. Privacy Issues in Location-based Services  Client requests information from the server related to her current location  Client wants to maintain privacy and anonymity  Location can be associated with user identity, e.g. service request at your own house  Thus client does not want the server to know her location  Server wants to release as precise information as possible 06/09/09ISI 2009, Dallas, Texas1

3. Existing Approaches  Cloaking: k -anonymity [3][4][5]  Client requests are sent to an anonymizer  Anonymizer “cloaks” client’s location to a region that include k -1 other clients  Anonymizer forwards queries to the server using the cloaked location  Need to trust the anonymizer 06/09/09ISI 2009, Dallas, Texas2

4. Existing Approaches … cont’d  Peer-to-peer [6][7]  A client c searches for k-1 peers  One peer acts as agent on behalf c  Chosen agent forwards requests to server using cloaked region  Need to be able to find k-1 peers  Need to trust the chosen agent peer 306/09/09ISI 2009, Dallas, Texas

5. Drawbacks of Existing Approaches  Need to trust the anonymizer or peers  Reveals some spatial information (general region of query)  Correlation attacks  Could possibly identify the client  Large volume of query results 06/09/09ISI 2009, Dallas, Texas4

6. Problem Definition and Motivation  Nearest Neighbor Query Example: Find me the nearest gas station from the location based server (LBS)  Goal: Find a way to protect privacy of the client while ensuring server returns precise data  Privacy means: no release of identity or location of the client  Motivation: Recent research shows PIR is a feasible and privacy-preserving approach, but server reveals too much data 506/09/09ISI 2009, Dallas, Texas

7. Our Approach  Focus on Exact-Nearest-Neighbour queries  Uses PIR framework by Shahabi et al. [1] as a first step  Applies Oblivious Transfer [2] as the second step (to make server data precise) 06/09/09ISI 2009, Dallas, Texas6

8. Private Information Retrieval (PIR)  Based on a computationally hard problem  Client sends an encrypted request for information  Server does not know what it reveals 06/09/09ISI 2009, Dallas, Texas7 E (i) Bob: X[ 1,2,3,…..,N ]Alice: Wants bit i v(X, E(i))

9. PIR Theory 806/09/09ISI 2009, Dallas, Texas

10. PIR in Location-based Services 06/09/09ISI 2009, Dallas, Texas9  User input: [ y 1,y 2,..,y n ]  Server computes: z r = Π n j=1 w (r,j)  w (r,j)= y j 2 if M r,j = 0 and w (r,j)= y j otherwise  Server returns: z = [ z 1, z 2,.., z n ]  User computes: If z a ε QR, M a,b = 0 else M a,b = 1

11. Example of PIR in LBS 06/09/09ISI 2009, Dallas, Texas10  User location: M 2,3  User generates request: y =[y 1,y 2,y 3,y 4 ]  y 3 ε QNR, y 1,y 2,y 4 ε QR  Server replies: [z 1,z 2,z 3, z 4 ]  If z 2 ε QR, M 2,3 = 0, else M 2,3 = 1

12. Oblivious Transfer  Fundamental cryptographic protocol  Alice asks for one bit of information from Bob  Alice does not get to know any other bit  Bob does not know what bit Alice asked for  Many variants: 1-of-2, 1-of-n, k-of-n 1106/09/09ISI 2009, Dallas, Texas

13. Example of Oblivious Transfer (OT) 1206/09/09ISI 2009, Dallas, Texas

14. Exampleof OT … cont’d 1306/09/09ISI 2009, Dallas, Texas

15. The Two-level Protocol: First Step 06/09/09ISI 2009, Dallas, Texas14  Server divides the area into Voronoi cells and superimposes a grid on it  Each grid cell has list of Points Of Interests (POIs) associated with it  One POI each in a Voronoi cell  Contents of grid cells are the list of POIs

16. First Step: PIR …. cont’d 06/09/09ISI 2009, Dallas, Texas15  Client requests a column corresponding to its grid cell using PIR: e.g. PIR(C)  Server prepares encrypted column C

17. Second Step – Oblivious Transfer (OT)  Client initiates 1-of-n OT with server  Client and server agree on a set of keys  Server encrypts each bit of PIR response with a different set of keys (according to the index of the bit) and sends it across  Server and client exchange keys (through 1-of-2 OT)  Client can decrypt the bit it wants and none else 1606/09/09ISI 2009, Dallas, Texas

18. High-level View  Client knows it location  Tries to execute PIR to get its cell  Server prepares PIR response corresponding to a column that the client is in and encrypts it  Client and server engage in 1-of-n OT to get client’s cell from the column 1706/09/09ISI 2009, Dallas, Texas

19. High-level View … cont’d  Contents of client’s grid cell are its neighbours (Point of Interests of POIs)  Client can easily calculate which point is the nearest  May contain redundant POIs  Repeated/redundant POIs can be discarded 1806/09/09ISI 2009, Dallas, Texas

20. Complexity  N : number of objects (POIs),  M: number of bits in each  Request by client: O(M · N)  Response by server: O(M · N + √N log √N)  Total time: O(M · N + √N log √N) 1906/09/09ISI 2009, Dallas, Texas

21. Comparison of Costs 2006/09/09ISI 2009, Dallas, Texas ActionPIROTOur Two Level Protocol Req. by user O(√n)O(logn) O(√n+log√n) Res. By server O(m  √n)O(m  n)O(m  √n) Total time O(m  √n) O(m  logn + m  n) O(m  √n+log√n)

22. Conclusion  Contribution: Proposed a two-level protocol for private location queries  PIR over the entire grid – large amount of data would be revealed  OT over the entire grid – very expensive  Our approach – reduces amount of data revealed, not very expensive  Future direction: alternative approach (multi-level PIR) 2106/09/09ISI 2009, Dallas, Texas

23. References 1. G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi and K.Tan. Private Queries in Location Based Services: Anonymizers are not Necessary. In Proc. of ACM SIGMOD 2008, pp. 121-132. 2. B. Pinkas and M. Naor. Efficient Oblivious Transfer Protocols. In Proc. Of 12 th ACM-SIAM Symposium on Discrete Algorithms. pp. 448-457, 2001. 3. B. Gedik and L. Liu. Privacy in mobile systems: A personalized anonymization model. In Proc. Of ICDCS. Pp. 620-629, 2005. 4. P. Kalnis, G. Ghinita, K. Mouratidis and D. Papadias. Preventing location-based identity inference in anonymous spatial queries. In Proc. Of IEEE TKDE, pp. 239-257, 2007. 2206/09/09ISI 2009, Dallas, Texas

24. References … cont’d 5. M. Mokbel, C. Chow and W. Aref. The new Casper: Query Processing for location-based services without compromising privacy. In Proc. Of VLDB, pp. 219-239, 2005. 6. C.Y. Chow, M. Mokbel and X. Liu. A peer-to-peer spatial cloaking algorithm for anonymous location-based services. In Proc. of ACM International Symposium on GIS. Pp. 247-256, 2006. 7. G. Ghinita, P. Kalnis and S. Skiadopoulos. PRIVE: Anonymous location-based queries in distributed mobile systems. In Proc. of 1 st Intl. Conference on World Wide Web (WWW), pp. 371-380, 2007. 2306/09/09ISI 2009, Dallas, Texas