Private Location-based Query Processing Using PIR Layla Pournajaf - PowerPoint PPT Presentation

Private Location-based Query Processing Using PIR Layla Pournajaf Guest Lecture Data Privacy and Security Nov. 2016 1 Computer Science Department Emory University

Motivation I want the list of nearest restaurants 2 Computer Science Department Emory University

Motivation I want the list of nearest restaurants Give me your location first 3 Computer Science Department Emory University

Private Information Retrieval Suppose that a server maintains a database consisting of N sequential blocks. PIR protocols enable a client to retrieve the i th block from the server, without the server discovering which block was requested (i.e., index i). 4 Computer Science Department Emory University

PIR Implementations • Computational PIR methods [3] rely on the computational intractability of well-known problems. However, they entail expensive operations linear in the database size, which lead to prohibitive processing costs (in the order of thousands of seconds even for moderate database sizes). • Secure hardware PIR [4] is currently the only practical PIR mechanism. It relies on a tamper-resistant CPU that is positioned at the server and is trusted by the clients. This CPU receives a client block request, which is unreadable by the server. It obliviously extracts the requested block from the server’s disk, and returns it to the client in an encrypted form decipherable solely by the client. This paradigm leads to constant communication cost, and amortized polylogarithmic computational cost. The latter translates to processing times close to a second even for Gigabyte databases. 5 Computer Science Department Emory University

Private Information Retrieval SCOP: A trusted secure coprocessor implementation of PIR protocol PIR- Aware SYSTEM MODEL Query Processing 6 Computer Science Department Emory University

Location-based Queries Using PIR • Server-side Indexing of Data • Spatial Indexing • Compact Storage • Enable data retrieval in minimal number of PIR requests • Client-side query processing • Query processing with minimal information • Retrieval of the same number of PIR requests for any query 7 Computer Science Department Emory University

Nearest Neighbor Query Using PIR [2]: Server-side Indexing of Data: Voronoi Tessellation 8 Computer Science Department Emory University

Nearest Neighbor Query Using PIR [2]: Server-side Indexing of Data: Voronoi Tessellation 9 Computer Science Department Emory University

Nearest Neighbor Query Using PIR [2] Computer Science Department 10 10 Emory University

Nearest Neighbor Queries Using PIR [2] • Server-side Indexing of Data • Spatial Indexing (Voronoi Tessellation) • Efficient Storage (Storage of max 3 points per cell) • Data retrieval in minimal number of PIR requests (1 request per query) • Client-side query processing • Query processing with minimal information (find the query cell index, request the index) • Retrieval of the same number of PIR requests for any query (1 request per query) 11 Computer Science Department Emory University

K-Nearest Neighbor Queries Using PIR [1] 12 Computer Science Department Emory University

K-Nearest Neighbor Queries Using PIR [1] • Server-side Indexing of Data • Spatial Indexing (Grid Structure) • Efficient Storage (Not very efficient (Too many dummy points)) • Data retrieval in minimal number of PIR requests (Empty cells may be requested with no use. Too many points in a cell may need several requests) • Client-side query processing • Query processing with minimal information • Retrieval of the same number of PIR requests for any query 13 Computer Science Department Emory University

K-Nearest Neighbor Queries Using PIR [1] • Server-side Indexing of Data • Spatial Indexing (Grid Structure) • Efficient Storage (Not very efficient (Too many dummy points)) • Data retrieval in minimal number of PIR requests (Empty cells may be requested with no use. Too many points in a cell may need several requests) • Client-side query processing • Query processing with minimal information • Retrieval of the same number of PIR requests for any query 14 Computer Science Department Emory University

Query Plan • Goal: • Ensuring the same number of PIR requests per query • Approach: • Finding the maximum number of PIR requests considering any query point (QP) • Regardless of the location of query, all users should ask for QP blocks 15 Computer Science Department Emory University

kNN – Smarter Indexing [1] Computer Science Department 16 16 Emory University

Query Plan 17 Computer Science Department Emory University

References [1] S. Papadopoulos, S. Bakiras, and D. Papadias , “Nearest neighbor search with strong location privacy,” Proceedings of the VLDB Endowment, vol. 3, no. 1-2, pp. 619 – 629, 2010. [2] A. Khoshgozaran, C. Shahabi, and H. Shirani-Mehr , “Location privacy: going beyond k- anonymity, cloaking and anonymizers,” Knowledge and Information Systems, vol. 26, no. 3, pp. 435 – 465, 2011. [3] G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.-L. Tan, “Private queries in location based services: anonymizers are not necessary,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, 2008, pp. 121 – 132. [4 ] P. Williams and R. Sion, “Usable pir .” in NDSS, 2008. Computer Science Department 18 Emory University

Thank You! Computer Science Department 19 Emory University

My Research: Reverse k-Nearest Neighbor Queries Using PIR  Finding RKNN query without LBS deducing the location of client. p2 is the nearest neighbor of q p1 and p4 are the reverse nearest neighbors of q

Reverse k-Nearest Neighbor Queries  Pruning  Shrink the search space by removing the points that can not be the answer  The remaining points are considered Candidates  Verification  Compute kNN for all candidates. For each candidate, if its kNN includes the query point, it is an answer of RkNN query

Pruning Strategies Computer Science Department 22 22 Emory University

Pruning Strategies Computer Science Department 23 23 Emory University

Reverse k-Nearest Neighbor Queries Using PIR: Server-Side Indexing Method 1  DB_cnt: < c:pre, c:count >  DB_loc: <p.id, p.x, p.y, p.ptr>  DB_dtl: <p:id, p:payload > Computer Science Department 24 24 Emory University

Solution 1 Overview  Aware of grid structure, query plan and DB_cnt Client  Pruning Phase  Determine candidate and influenced cells  Verification Phase  Retrieve the location coordination and find the results  Indexing Data Server  Create Hilbert curve on fix grid  Create DB_cnt, DB_loc and DB_dtl  Query Plan  Uncertain half-plane pruning for uncertain queries  Modified Verification Computer Science Department 25 Emory University

Pruning method in Query Plan  Uncertain half-plane pruning for uncertain queries H M:B N O H P:A R P M C H’ N:E B H’ M:B A Q H’ O:D E D H’ P:A 26 Computer Science Department Emory University

Reverse k-Nearest Neighbor Queries Using PIR: Server-Side Indexing Method 2  Hilbert-Rtree: hybrid structure based on B+-tree and R-tree  Internal nodes : < MBR, HLV, Ptr >  leaf nodes : < mbr, Pid >  Nodes and points reside in HRT based on their Hilbert number.  nodes are separated based on the largest Hilbert value  In case of overflow in a node, it is handled by applying s, s+1 policy. Computer Science Department 27 27 Emory University

Solution 2 Overview  Aware of Hilbert-Rtree structure, query plan and Client level_0 of HRT (< MBR, LHV, c >) Advantage: Instead of probing small cell  Pruning Phase of Grid, assess the MBR in  Verification Phase which might contain more cells  Indexing Data Server  Create Hilbert-Rtree structure  Create DB_loc and DB_dtl  Query Plan  Uncertain half-plane pruning for uncertain queries  Modified Verification Computer Science Department 28 Emory University

Thank You! Computer Science Department 29 Emory University