7,799 research outputs found
Privacy-Preserving Matching Protocols for Attributes and Strings
In this technical report we present two new privacy-preserving matching protocols for singular attributes and strings, respectively. The first one is used for matching of common attributes without revealing unmatched ones to each other. The second protocol is used to discover the longest common sub-string of two input strings in a privacy-preserving manner. Compared with previous work, our solutions are efficient and suitable to implement for many different applications, e.g., discovery of common worm signatures, computation of similarity of IP payloads
Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity
Consider two parties who want to compare their strings, e.g., genomes, but do
not want to reveal them to each other. We present a system for
privacy-preserving matching of strings, which differs from existing systems by
providing a deterministic approximation instead of an exact distance. It is
efficient (linear complexity), non-interactive and does not involve a third
party which makes it particularly suitable for cloud computing. We extend our
protocol, such that it mitigates iterated differential attacks proposed by
Goodrich. Further an implementation of the system is evaluated and compared
against current privacy-preserving string matching algorithms.Comment: 6 pages, 4 figure
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
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
Enabling Confidentiality in Content-Based Publish/Subscribe Infrastructures
Content-Based Publish/Subscribe (CBPS) is an interaction model where the interests of subscribers are stored in a content-based forwarding infrastructure to guide routing of notifications to interested parties. In this paper, we focus on answering the following question: Can we implement content-based publish/subscribe while keeping subscriptions and notifications confidential from the forwarding brokers? Our contributions include a systematic analysis of the problem, providing a formal security model and showing that the maximum level of attainable security in this setting is restricted. We focus on enabling provable confidentiality for commonly used applications and subscription languages in CBPS and present a series of practical provably secure protocols, some of which are novel and others adapted from existing work. We have implemented these protocols in SIENA, a popular CBPS system. Evaluation results show that confidential content-based publish/subscribe is practical: A single broker serving 1000 subscribers is able to route more than 100 notifications per second with our solutions
Security and Efficiency Analysis of the Hamming Distance Computation Protocol Based on Oblivious Transfer
open access articleBringer et al. proposed two cryptographic protocols for the computation of Hamming distance. Their first scheme uses Oblivious Transfer and provides security in the semi-honest model. The other scheme uses Committed Oblivious Transfer and is claimed to provide full security in the malicious case. The proposed protocols have direct implications to biometric authentication schemes between a prover and a verifier where the verifier has biometric data of the users in plain form.
In this paper, we show that their protocol is not actually fully secure against malicious adversaries. More precisely, our attack breaks the soundness property of their protocol where a malicious user can compute a Hamming distance which is different from the actual value. For biometric authentication systems, this attack allows a malicious adversary to pass the authentication without knowledge of the honest user's input with at most complexity instead of , where is the input length. We propose an enhanced version of their protocol where this attack is eliminated. The security of our modified protocol is proven using the simulation-based paradigm. Furthermore, as for efficiency concerns, the modified protocol utilizes Verifiable Oblivious Transfer which does not require the commitments to outputs which improves its efficiency significantly
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