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Practical Private Computation of Vector Addition-Based FunctionsPowerPoint Presentation

Practical Private Computation of Vector Addition-Based Functions

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Practical Private Computation of Vector Addition-Based Functions

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Practical Private Computation of Vector Addition-Based Functions

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Yitao Duan and John Canny

Computer Science Division

University of California, Berkeley

PODC 2007, August 12, Portland OR

A method for performing privacy preserving distributed computation of many algorithms that is practical and secure in a realistic threat model at large scale

Provably strong (information-theoretic) privacy

Efficient ZKP to deal with cheating users

- A few collaborating data miners mining data from n users
- Each user has an m-dimensional vector
- Realistic scale: m, n large (103–106)
- Threat: data miners are passive, users are allowed to cheat arbitrarily

Challenge: standard cryptographic tools not feasible at this scale

- Private computation based on secret sharing using addition only steps
- Private addition is much simpler than multiplication
- The main computation is in small field (32 or 64 bits) – private computation has the same cost as regular arithmetic
- A lot of (nonlinear) algorithms can be done with addition: Regression, Classification, Bayes net, Link analysis, SVD, EM.

- An extremely efficient ZKP that the L2 norm of user vector is bounded by L (Only O(logm) large field operations)

The server asks for N random projections of the user’s vector, the user proves the square sum of them is small.

Projections are done in small field. The only large field operations are N encryptions and boundedness ZKP

O(log m) public key crypto operations (instead of O(m)) to prove that the L-2 norm of an m-dim vector is smaller than L.

(a) Linear and (b) log plots of probability of user input acceptance as a function of |d|/L for N = 50. (b) also includes probability of rejection. In each case, the steepest (jagged curve) is the single-value vector (case 3), the middle curve is Zipf vector (case 2) and the shallow curve is uniform vector (case 1)

(a) Verifier and (b) prover times in seconds with N = 50, where (from top to bottom) L has 40, 20, or 10 bits. The x-axis is the vector length m.

- Code available for download, soon.
- duan@cs.berkeley.edu
- http://www.cs.berkeley.edu/~duan
- Thank you!