6,192 research outputs found
On Communication Protocols that Compute Almost Privately
A traditionally desired goal when designing auction mechanisms is incentive
compatibility, i.e., ensuring that bidders fare best by truthfully reporting
their preferences. A complementary goal, which has, thus far, received
significantly less attention, is to preserve privacy, i.e., to ensure that
bidders reveal no more information than necessary. We further investigate and
generalize the approximate privacy model for two-party communication recently
introduced by Feigenbaum et al.[8]. We explore the privacy properties of a
natural class of communication protocols that we refer to as "dissection
protocols". Dissection protocols include, among others, the bisection auction
in [9,10] and the bisection protocol for the millionaires problem in [8].
Informally, in a dissection protocol the communicating parties are restricted
to answering simple questions of the form "Is your input between the values
\alpha and \beta (under a predefined order over the possible inputs)?".
We prove that for a large class of functions, called tiling functions, which
include the 2nd-price Vickrey auction, there always exists a dissection
protocol that provides a constant average-case privacy approximation ratio for
uniform or "almost uniform" probability distributions over inputs. To establish
this result we present an interesting connection between the approximate
privacy framework and basic concepts in computational geometry. We show that
such a good privacy approximation ratio for tiling functions does not, in
general, exist in the worst case. We also discuss extensions of the basic setup
to more than two parties and to non-tiling functions, and provide calculations
of privacy approximation ratios for two functions of interest.Comment: to appear in Theoretical Computer Science (series A
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
Privacy-Friendly Collaboration for Cyber Threat Mitigation
Sharing of security data across organizational boundaries has often been
advocated as a promising way to enhance cyber threat mitigation. However,
collaborative security faces a number of important challenges, including
privacy, trust, and liability concerns with the potential disclosure of
sensitive data. In this paper, we focus on data sharing for predictive
blacklisting, i.e., forecasting attack sources based on past attack
information. We propose a novel privacy-enhanced data sharing approach in which
organizations estimate collaboration benefits without disclosing their
datasets, organize into coalitions of allied organizations, and securely share
data within these coalitions. We study how different partner selection
strategies affect prediction accuracy by experimenting on a real-world dataset
of 2 billion IP addresses and observe up to a 105% prediction improvement.Comment: This paper has been withdrawn as it has been superseded by
arXiv:1502.0533
Secure two-party quantum evaluation of unitaries against specious adversaries
We describe how any two-party quantum computation, specified by a unitary
which simultaneously acts on the registers of both parties, can be privately
implemented against a quantum version of classical semi-honest adversaries that
we call specious. Our construction requires two ideal functionalities to
garantee privacy: a private SWAP between registers held by the two parties and
a classical private AND-box equivalent to oblivious transfer. If the unitary to
be evaluated is in the Clifford group then only one call to SWAP is required
for privacy. On the other hand, any unitary not in the Clifford requires one
call to an AND-box per R-gate in the circuit. Since SWAP is itself in the
Clifford group, this functionality is universal for the private evaluation of
any unitary in that group. SWAP can be built from a classical bit commitment
scheme or an AND-box but an AND-box cannot be constructed from SWAP. It follows
that unitaries in the Clifford group are to some extent the easy ones. We also
show that SWAP cannot be implemented privately in the bare model
Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing
In this paper, we propose a privacy preserving distributed
clustering protocol for horizontally partitioned data based on a very efficient
homomorphic additive secret sharing scheme. The model we use
for the protocol is novel in the sense that it utilizes two non-colluding
third parties. We provide a brief security analysis of our protocol from
information theoretic point of view, which is a stronger security model.
We show communication and computation complexity analysis of our
protocol along with another protocol previously proposed for the same
problem. We also include experimental results for computation and communication
overhead of these two protocols. Our protocol not only outperforms
the others in execution time and communication overhead on
data holders, but also uses a more efficient model for many data mining
applications
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