Efficient Privacy-Preserving Association Rule Mining: P4P Style
Yitao Duan
Canny, J.
Zhan, J.
Comput. Sci. Div., California Univ., Berkeley, CA;
This paper appears in: Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Publication Date: March 1 2007-April 5 2007
On page(s): 654-660
Location: Honolulu, HI,
ISBN: 1-4244-0705-2
INSPEC Accession Number: 9507164
Digital Object Identifier: 10.1109/CIDM.2007.368938
Current Version Published: 2007-06-04
Abstract
In this paper we introduce a new practical framework, called P4P (peers for privacy), for privacy-preserving data mining. P4P features a hybrid architecture combining P2P and client-server paradigms and provides practical private protocols for user data validation and general computation. The architecture is guided by the natural incentives of the participants and allows the computation to be based on verifiable secret sharing (VSS) where arithmetic operations are done over small fields (e.g. 32 or 64 bits), so that private arithmetic operations have the same cost as normal arithmetic. Verification of user data, which uses large-field public-key arithmetic (1024 bits or more) and homomorphic computation, only requires a small number (constant or logarithmic in the size of user data) of large integer operations. The solution is extremely efficient: In experiments with our implementation, verification of a million-element vector takes a few seconds of server or client time on commodity PCs (in contrast, using standard techniques takes hours). This verification can be used in many privacy-preserving data mining tasks to detect cheating users who attempt to bias the computation by submitting exaggerated values as their inputs. As an example, we demonstrate how association rule mining can be done in the P4P model with near-optimal efficiency and provable privacy
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