Browsing by Autor "David Karpuk"

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    Private Computation of Systematically Encoded Data with Colluding\n Servers
    (Cornell University, 2018) David Karpuk
    Private Computation (PC), recently introduced by Sun and Jafar, is a\ngeneralization of Private Information Retrieval (PIR) in which a user wishes to\nprivately compute an arbitrary function of data stored across several servers.\nWe construct a PC scheme which accounts for server collusion, coded data, and\nnon-linear functions. For data replicated over several possibly colluding\nservers, our scheme computes arbitrary functions of the data with rate equal to\nthe asymptotic capacity of PIR for this setup. For systematically encoded data\nstored over colluding servers, we privately compute arbitrary functions of the\ncolumns of the data matrix and calculate the rate explicitly for polynomial\nfunctions. The scheme is a generalization of previously studied star-product\nPIR schemes.\n
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    Private Proximity Retrieval
    (2019) Tuvi Etzion; Oliver W. Gnilke; David Karpuk; Eitan Yaakobi; Yiwei Zhang
    A private proximity retrieval (PPR) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance r from the user's record x. The user's privacy at each server is given by the fraction of the record x that is kept private. The distortion of a PPR scheme measures how accurately the user can calculate the identities of the desired files. We assume that each server stores a copy of the database. This paper studies protocols that offer trade-offs between perfect privacy and low computational complexity and storage.In this paper, this study is initiated. The work focuses on the case when the records are binary vectors together with the Hamming distance. In particular, for a given privacy level, we investigate the minimum number of servers that guarantee a prescribed distortion value. The collusions of pairs of servers as well as other distance measures are investigated.