914 research outputs found
A Practical Set-Membership Proof for Privacy-Preserving NFC Mobile Ticketing
To ensure the privacy of users in transport systems, researchers are working
on new protocols providing the best security guarantees while respecting
functional requirements of transport operators. In this paper, we design a
secure NFC m-ticketing protocol for public transport that preserves users'
anonymity and prevents transport operators from tracing their customers' trips.
To this end, we introduce a new practical set-membership proof that does not
require provers nor verifiers (but in a specific scenario for verifiers) to
perform pairing computations. It is therefore particularly suitable for our
(ticketing) setting where provers hold SIM/UICC cards that do not support such
costly computations. We also propose several optimizations of Boneh-Boyen type
signature schemes, which are of independent interest, increasing their
performance and efficiency during NFC transactions. Our m-ticketing protocol
offers greater flexibility compared to previous solutions as it enables the
post-payment and the off-line validation of m-tickets. By implementing a
prototype using a standard NFC SIM card, we show that it fulfils the stringent
functional requirement imposed by transport operators whilst using strong
security parameters. In particular, a validation can be completed in 184.25 ms
when the mobile is switched on, and in 266.52 ms when the mobile is switched
off or its battery is flat
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters
Data privacy is crucial when dealing with biometric data. Accounting for the
latest European data privacy regulation and payment service directive,
biometric template protection is essential for any commercial application.
Ensuring unlinkability across biometric service operators, irreversibility of
leaked encrypted templates, and renewability of e.g., voice models following
the i-vector paradigm, biometric voice-based systems are prepared for the
latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean
and cosine comparators are known to ensure data privacy demands, without loss
of discrimination nor calibration performance. Bridging gaps from template
protection to speaker recognition, two architectures are proposed for the
two-covariance comparator, serving as a generative model in this study. The
first architecture preserves privacy of biometric data capture subjects. In the
second architecture, model parameters of the comparator are encrypted as well,
such that biometric service providers can supply the same comparison modules
employing different key pairs to multiple biometric service operators. An
experimental proof-of-concept and complexity analysis is carried out on the
data from the 2013-2014 NIST i-vector machine learning challenge
Threshold cryptography based on AsmuthâBloom secret sharing
Cataloged from PDF version of article.In this paper, we investigate how threshold cryptography can be conducted with the Asmuth-Bloom secret sharing scheme and present three novel function sharing schemes for RSA, ElGamal and Paillier cryptosysterns. To the best of our knowledge, these are the first provably secure threshold cryptosystems realized using the Asmuth-Bloom secret sharing. Proposed schemes are comparable in performance to earlier proposals in threshold cryptography. (c) 2007 Elsevier Inc. All rights reserved
A practical and secure multi-keyword search method over encrypted cloud data
Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective
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