Efficient Private Matching and Set Intersection

  • Michael J. Freedman
  • Kobbi Nissim
  • Benny Pinkas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3027)


We consider the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain. This problem has many applications for online collaboration. We present protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments. For lists of length k, we obtain O(k) communication overhead and O(k ln ln k) computation. The protocol for the semi-honest environment is secure in the standard model, while the protocol for the malicious environment is secure in the random oracle model. We also consider the problem of approximating the size of the intersection, show a linear lower-bound for the communication overhead of solving this problem, and provide a suitable secure protocol. Lastly, we investigate other variants of the matching problem, including extending the protocol to the multi-party setting as well as considering the problem of approximate matching.


Encryption Scheme Trusted Third Party Homomorphic Encryption Random Oracle Model Oblivious Transfer 
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 2004

Authors and Affiliations

  • Michael J. Freedman
    • 1
  • Kobbi Nissim
    • 2
  • Benny Pinkas
    • 3
  1. 1.New York University 
  2. 2.Microsoft Research SVC 
  3. 3.HP Labs 

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