Cryptanalysis on a novel unconditionally secure oblivious polynomial evaluation protocol

Vanishree et.al proposed a novel unconditionally oblivious polynomial evaluation protocol and they claimed that can fulfill both sender and receiver’s security. Here, this protocol is cryptanalyzed. We find that it has a fatal fault which cannot implement the receiver’s security at all and show the detail analyzing process

Tor Bridge Distribution Powered by Threshold RSA

Since its inception, Tor has offered anonymity for internet users around the world. Tor now offers bridges to help users evade internet censorship, but the primary distribution schemes that provide bridges to users in need have come under attack. This thesis explores how threshold RSA can help strengthen Tor\u27s infrastructure while also enabling more powerful bridge distribution schemes. We implement a basic threshold RSA signature system for the bridge authority and a reputation-based social network design for bridge distribution. Experimental results are obtained showing the possibility of quick responses to requests from honest users while maintaining both the secrecy and the anonymity of registered clients and bridges

Group Structure in Correlations and Its Applications in Cryptography

Correlated random variables are a key tool in cryptographic applications like secure multi-party computation. We investigate the power of a class of correlations that we term group correlations: A group correlation is a uniform distribution over pairs $(x,y) \in G^2$ such that $x+y\in S$, where $G$ is a (possibly non-abelian) group and $S$ is a subset of $G$. We also introduce bi-affine correlations and show how they relate to group correlations. We present several structural results, new protocols, and applications of these correlations. The new applications include a completeness result for black-box group computation, perfectly secure protocols for evaluating a broad class of black box ``mixed-groups\u27\u27 circuits with bi-affine homomorphism, and new information-theoretic results. Finally, we uncover a striking structure underlying OLE: In particular, we show that OLE over $\mathrm{GF}(2^n)$, is isomorphic to a group correlation over $\mathbb{Z}_4^n$

An implementation of the Paillier crypto system with threshold decryption without a trusted dealer

We consider the problem of securely generating the keys of the Paillier crypto system [11] with (t, n) threshold decryption, without a trusted dealer. Nishide and Sakurai [10] describe a solution, secure in the malicious model. We use their ideas to make a simpler solution for the semi-honest model, and further introduce a few optimisations. We implement the secure key generation protocol on a single computer, and consider its performance

PILOT : Practical Privacy-Preserving Indoor Localization Using OuTsourcing

In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS. In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500× faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day.Peer reviewe

Secure Two-party Computation Approach for NTRUEncrypt

Secure multi-party computation can provide a solution for privacy protection and ensure the correctness of the final calculation results. Lattice-based algorithms are considered to be one of the most promising post-quantum cryptographic algorithms due to a better balance among security, key sizes and calculation speeds. The NTRUEncrypt is a lattice-based anti-quantum attack cryptographic algorithm. Since there haven\u27t been much candidate post-quantum cryptographic algorithms for secure multi-party computation. In this paper, we propose a novel secure two-party computation scheme based on NTRUEncrypt and implement the polynomial multiplication operations under NTRUEncrypt-OT. Our secure two-party computation scheme mainly uses oblivious transfer and privacy set interaction. We prove the security of our scheme in the semi-honest model. Our scheme can be applied for multi-party computation scenarios, such as quantum attack-resisted E-votes or E-auctions

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-accurate Deep Neural Networks (DNNs). However, as DNNs are trained on data, a key question is how such models can be also trained securely. The few prior works on secure DNN training have focused either on designing custom protocols for existing training algorithms, or on developing tailored training algorithms and then applying generic secure protocols. In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts. We present QUOTIENT, a new method for discretized training of DNNs, along with a customized secure two-party protocol for it. QUOTIENT incorporates key components of state-of-the-art DNN training such as layer normalization and adaptive gradient methods, and improves upon the state-of-the-art in DNN training in two-party computation. Compared to prior work, we obtain an improvement of 50X in WAN time and 6% in absolute accuracy

Efficient Secure Multiparty Computation for Multidimensional Arithmetics and Its Application in Privacy-Preserving Biometric Identification

Over years of the development of secure multi-party computation (MPC), many sophisticated functionalities have been made pratical and multi-dimensional operations occur more and more frequently in MPC protocols, especially in protocols involving datasets of vector elements, such as privacy-preserving biometric identification and privacy-preserving machine learning. In this paper, we introduce a new kind of correlation, called tensor triples, which is designed to make multi-dimensional MPC protocols more efficient. We will discuss the generation process, the usage, as well as the applications of tensor triples and show that it can accelerate privacy-preserving biometric identification protocols, such as FingerCode, Eigenfaces and FaceNet, by more than 1000 times