Private Multiparty Sampling and Approximation of Vector Combinations

  • Yuval Ishai
  • Tal Malkin
  • Martin J. Strauss
  • Rebecca N. Wright
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4596)


We consider the problem of private efficient data mining of vertically-partitioned databases. Each of several parties holds a column of a data matrix (a vector) and the parties want to investigate the componentwise combination of their vectors. The parties want to minimize communication and local computation while guaranteeing privacy in the sense that no party learns more than necessary. Sublinear-communication private protocols have been primarily been studied only in the two-party case. We give efficient multiparty protocols for sampling a row of the data matrix and for computing arbitrary functions of a row, where the row index is additively shared among two or more parties. We also give protocols for approximating the componentwise sum, minimum, or maximum of the columns in which the communication and the number of public-key operations are at most polynomial in the size of the small approximation and polylogarithmic in the number of rows.


Oblivious Transfer Private Information Retrieval Secure Multiparty Computation Vector Combination Secure Function Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yuval Ishai
    • 1
  • Tal Malkin
    • 2
  • Martin J. Strauss
    • 3
  • Rebecca N. Wright
    • 4
  1. 1.Computer Science Dept., Technion, Haifa 32000Israel
  2. 2.Dept. of Computer Science, Columbia University, New York, NY 10025USA
  3. 3.Depts. of Math and EECS, University of Michigan, Ann Arbor, MI 48109USA
  4. 4.Computer Science Dept., Stevens Institute of Technology, Hoboken, NJ 07030USA

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