6,598 research outputs found
Secure and Efficient Two-party Quantum Scalar Product Protocol With Application to Privacy-preserving Matrix Multiplication
Secure two-party scalar product (S2SP) is a promising research area within
secure multiparty computation (SMC), which can solve a range of SMC problems,
such as intrusion detection, data analysis, and geometric computations.
However, existing quantum S2SP protocols are not efficient enough, and the
complexity is usually close to exponential level. In this paper, a novel secure
two-party quantum scalar product (S2QSP) protocol based on Fourier entangled
states is proposed to achieve higher efficiency. Firstly, the definition of
unconditional security under malicious models is given. And then, an honesty
verification method called Entanglement Bondage is proposed, which is used in
conjunction with the modular summation gate to resist malicious attacks. The
property of Fourier entangled states is used to calculate the scalar product
with polynomial complexity. The unconditional security of our protocol is
proved, which guarantees the privacy of all parties. In addition, we design a
privacy-preserving quantum matrix multiplication protocol based on S2QSP
protocol. By transforming matrix multiplication into a series of scalar product
processes, the product of two private matrices is calculated without revealing
any privacy. Finally, we show our protocol's feasibility in IBM Qiskit
simulator.Comment: 15 pages, 4 figure
Secret charing vs. encryption-based techniques for privacy preserving data mining
Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach
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Privacy-Preserving iVector-Based Speaker Verification
This paper introduces an efficient algorithm to develop a privacy-preserving voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice print instead of alphanumeric passwords. Since a voice print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this paper proposes a novel technique based on randomization to carry out voice authentication, which allows the user to enrol and verify their voice in the randomized domain. To achieve this, the iVector-based voice verification technique has been redesigned to work on the randomized domain. The proposed algorithm is validated using a well-known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomization operations
Private Multi-party Matrix Multiplication and Trust Computations
This paper deals with distributed matrix multiplication. Each player owns
only one row of both matrices and wishes to learn about one distinct row of the
product matrix, without revealing its input to the other players. We first
improve on a weighted average protocol, in order to securely compute a
dot-product with a quadratic volume of communications and linear number of
rounds. We also propose a protocol with five communication rounds, using a
Paillier-like underlying homomorphic public key cryptosystem, which is secure
in the semi-honest model or secure with high probability in the malicious
adversary model. Using ProVerif, a cryptographic protocol verification tool, we
are able to check the security of the protocol and provide a countermeasure for
each attack found by the tool. We also give a randomization method to avoid
collusion attacks. As an application, we show that this protocol enables a
distributed and secure evaluation of trust relationships in a network, for a
large class of trust evaluation schemes.Comment: Pierangela Samarati. SECRYPT 2016 : 13th International Conference on
Security and Cryptography, Lisbonne, Portugal, 26--28 Juillet 2016. 201
Enabling Privacy-preserving Auctions in Big Data
We study how to enable auctions in the big data context to solve many
upcoming data-based decision problems in the near future. We consider the
characteristics of the big data including, but not limited to, velocity,
volume, variety, and veracity, and we believe any auction mechanism design in
the future should take the following factors into consideration: 1) generality
(variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness
and verifiability (veracity). In this paper, we propose a privacy-preserving
construction for auction mechanism design in the big data, which prevents
adversaries from learning unnecessary information except those implied in the
valid output of the auction. More specifically, we considered one of the most
general form of the auction (to deal with the variety), and greatly improved
the the efficiency and scalability by approximating the NP-hard problems and
avoiding the design based on garbled circuits (to deal with velocity and
volume), and finally prevented stakeholders from lying to each other for their
own benefit (to deal with the veracity). We achieve these by introducing a
novel privacy-preserving winner determination algorithm and a novel payment
mechanism. Additionally, we further employ a blind signature scheme as a
building block to let bidders verify the authenticity of their payment reported
by the auctioneer. The comparison with peer work shows that we improve the
asymptotic performance of peer works' overhead from the exponential growth to a
linear growth and from linear growth to a logarithmic growth, which greatly
improves the scalability
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