Computer Science – Cryptography and Security
Scientific paper
2010-12-12
Computer Science
Cryptography and Security
Scientific paper
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.
Asuncion Arthur U.
Goodrich Michael T.
No associations
LandOfFree
Nonadaptive Mastermind Algorithms for String and Vector Databases, with Case Studies does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Nonadaptive Mastermind Algorithms for String and Vector Databases, with Case Studies, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonadaptive Mastermind Algorithms for String and Vector Databases, with Case Studies will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-636848