Privacy-preserving remote diagnostics

  • Authors:
  • Justin Brickell;Donald E. Porter;Vitaly Shmatikov;Emmett Witchel

  • Affiliations:
  • The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX

  • Venue:
  • Proceedings of the 14th ACM conference on Computer and communications security
  • Year:
  • 2007

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Abstract

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.