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Issue No. 01 - Jan. (2013 vol. 25)
ISSN: 1041-4347
pp: 131-144
A. U. Asuncion , Dept. of Comput. Sci., Univ. of California, Irvine, Irvine, CA, USA
M. T. Goodrich , Dept. of Comput. Sci., Univ. of California, Irvine, Irvine, CA, USA
In this paper, we study sparsity-exploiting Mastermind algorithms for attacking the privacy of an entire database of character strings or vectors, such as DNA strings, movie ratings, or social network friendship data. Based on reductions to nonadaptive group testing, our methods are able to take advantage of minimal amounts of privacy leakage, such as contained in a single bit that indicates if two people in a medical database have any common genetic mutations, or if two people have any common friends in an online social network. We analyze our Mastermind attack algorithms using theoretical characterizations that provide sublinear bounds on the number of queries needed to clone the database, as well as experimental tests on genomic information, collaborative filtering data, and online social networks. By taking advantage of the generally sparse nature of these real-world databases and modulating a parameter that controls query sparsity, we demonstrate that relatively few nonadaptive queries are needed to recover a large majority of each database.
string matching, collaborative filtering, data privacy, medical administrative data processing, query processing, social networking (online), nonadaptive queries, nonadaptive Mastermind algorithms, vector databases, sparsity-exploiting Mastermind algorithms, database privacy attack, character string database, nonadaptive group testing, privacy leakage, medical database, genetic mutations, online social network, Mastermind attack algorithms, sublinear bounds, database querying, genomic information, collaborative filtering data, query sparsity control, Databases, Data privacy, Social network services, Testing, Protocols, Cloning, nonadaptive attacks, Mastermind algorithms, privacy leaks, data cloning, combinatorial group testing

A. U. Asuncion and M. T. Goodrich, "Nonadaptive Mastermind Algorithms for String and Vector Databases, with Case Studies," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 131-144, 2013.
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