Privacy-preserving remote diagnostics

13 years 3 days ago
Privacy-preserving remote diagnostics
We present an efficient protocol for privacy-preserving evaluation of diagnostic programs, represented as binary decision trees or branching programs. The protocol applies a branching diagnostic program with classification labels in the leaves to the user’s attribute vector. The user learns only the label assigned by the program to his vector; the diagnostic program itself remains secret. The program’s owner does not learn anything. Our construction is significantly more efficient than those obtained by direct application of generic secure multi-party computation techniques. We use our protocol to implement a privacy-preserving version of the Clarify system for software fault diagnosis, and demonstrate that its performance is acceptable for many practical scenarios. Categories and Subject Descriptors E.3 [Data]: Data Encryption; I.2.1 [Artificial Intelligence]: Applications and Expert Systems General Terms Algorithms, Security, Performance Keywords Privacy, Data Mining, Diagn...
Justin Brickell, Donald E. Porter, Vitaly Shmatiko
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CCS
Authors Justin Brickell, Donald E. Porter, Vitaly Shmatikov, Emmett Witchel
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