1,509 research outputs found
EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity
Electronic information is increasingly often shared among entities without
complete mutual trust. To address related security and privacy issues, a few
cryptographic techniques have emerged that support privacy-preserving
information sharing and retrieval. One interesting open problem in this context
involves two parties that need to assess the similarity of their datasets, but
are reluctant to disclose their actual content. This paper presents an
efficient and provably-secure construction supporting the privacy-preserving
evaluation of sample set similarity, where similarity is measured as the
Jaccard index. We present two protocols: the first securely computes the
(Jaccard) similarity of two sets, and the second approximates it, using MinHash
techniques, with lower complexities. We show that our novel protocols are
attractive in many compelling applications, including document/multimedia
similarity, biometric authentication, and genetic tests. In the process, we
demonstrate that our constructions are appreciably more efficient than prior
work.Comment: A preliminary version of this paper was published in the Proceedings
of the 7th ESORICS International Workshop on Digital Privacy Management (DPM
2012). This is the full version, appearing in the Journal of Computer
Securit
Flexible and Robust Privacy-Preserving Implicit Authentication
Implicit authentication consists of a server authenticating a user based on
the user's usage profile, instead of/in addition to relying on something the
user explicitly knows (passwords, private keys, etc.). While implicit
authentication makes identity theft by third parties more difficult, it
requires the server to learn and store the user's usage profile. Recently, the
first privacy-preserving implicit authentication system was presented, in which
the server does not learn the user's profile. It uses an ad hoc two-party
computation protocol to compare the user's fresh sampled features against an
encrypted stored user's profile. The protocol requires storing the usage
profile and comparing against it using two different cryptosystems, one of them
order-preserving; furthermore, features must be numerical. We present here a
simpler protocol based on set intersection that has the advantages of: i)
requiring only one cryptosystem; ii) not leaking the relative order of fresh
feature samples; iii) being able to deal with any type of features (numerical
or non-numerical).
Keywords: Privacy-preserving implicit authentication, privacy-preserving set
intersection, implicit authentication, active authentication, transparent
authentication, risk mitigation, data brokers.Comment: IFIP SEC 2015-Intl. Information Security and Privacy Conference, May
26-28, 2015, IFIP AICT, Springer, to appea
P2KMV: A Privacy-preserving Counting Sketch for Efficient and Accurate Set Intersection Cardinality Estimations
In this paper, we propose P2KMV, a novel privacy-preserving counting sketch, based on the k minimum values algorithm. With P2KMV, we offer a versatile privacy-enhanced technology for obtaining statistics, following the principle of data minimization, and aiming for the sweet spot between privacy, accuracy, and computational efficiency. As our main contribution, we develop methods to perform set operations, which facilitate cardinality estimates under strong privacy requirements. Most notably, we propose an efficient, privacy-preserving algorithm to estimate the set intersection cardinality. P2KMV provides plausible deniability for all data items contained in the sketch. We discuss the algorithm's privacy guarantees as well as the accuracy of the obtained estimates. An experimental evaluation confirms our analytical expectations and provides insights regarding parameter choices
Learning Character Strings via Mastermind Queries, with a Case Study Involving mtDNA
We study the degree to which a character string, , leaks details about
itself any time it engages in comparison protocols with a strings provided by a
querier, Bob, even if those protocols are cryptographically guaranteed to
produce no additional information other than the scores that assess the degree
to which matches strings offered by Bob. We show that such scenarios allow
Bob to play variants of the game of Mastermind with so as to learn the
complete identity of . We show that there are a number of efficient
implementations for Bob to employ in these Mastermind attacks, depending on
knowledge he has about the structure of , which show how quickly he can
determine . Indeed, we show that Bob can discover using a number of
rounds of test comparisons that is much smaller than the length of , under
reasonable assumptions regarding the types of scores that are returned by the
cryptographic protocols and whether he can use knowledge about the distribution
that comes from. We also provide the results of a case study we performed
on a database of mitochondrial DNA, showing the vulnerability of existing
real-world DNA data to the Mastermind attack.Comment: Full version of related paper appearing in IEEE Symposium on Security
and Privacy 2009, "The Mastermind Attack on Genomic Data." This version
corrects the proofs of what are now Theorems 2 and 4
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