Biblio

Verifiability is one of the parameters in e-voting that can increase confidence in voting technology with several parties ensuring that voters do not change their votes. Voting has become an important part of the democratization system, both to make choices regarding policies, to elect representatives to sit in the representative assembly, and to elect leaders. the more voters and the wider the distribution, the more complex the social life, and the need to manage the voting process efficiently and determine the results more quickly, electronic-based voting (e-Voting) is becoming a more promising option. The level of confidence in voting depends on the capabilities of the system. E-voting must have parameters that can be used as guidelines, which include the following: Accuracy, Invulnerability, Privacy and Verifiability. The implementation of the simple verifiability metric to measure the degree of verifiability in the e-voting protocol, the researchers can calculate the degree of verifiability in the e-voting protocol and the researchers have been able to assess the proposed e-voting protocol with the standard of the best degree of verifiability is 1, where the value of 1 is is absolutely verified protocol.

Big Data has rapidly developed into a hot research topic in many areas that attracts attention from academia and industry around the world. Many organization demands efficient solution to store, process, analyze and search huge amount of information. With the rapid development of cloud computing, organization prefers cloud storage services to reduce the overhead of storing data locally. However, the security and privacy of big data in cloud computing is a major source of concern. One of the positive ways of protecting data is encrypting it before outsourcing to remote servers, but the encrypted significant amounts of cloud data brings difficulties for the remote servers to perform any keyword search functions without leaking information. Various privacy-preserving keyword search (PPKS) schemes have been proposed to mitigate the privacy issue of big data encrypted on cloud storage. This paper presents an extensive analysis of the existing PPKS techniques in terms of verifiability, efficiency and data privacy. Through this analysis, we present some valuable directions for future work.

Data outsourcing to cloud has been a common IT practice nowadays due to its significant benefits. Meanwhile, security and privacy concerns are critical obstacles to hinder the further adoption of cloud. Although data encryption can mitigate the problem, it reduces the functionality of query processing, e.g., disabling SQL queries. Several schemes have been proposed to enable one-dimensional query on encrypted data, but multi-dimensional range query has not been well addressed. In this paper, we propose a secure and scalable scheme that can support multi-dimensional range queries over encrypted data. The proposed scheme has three salient features: (1) Privacy: the server cannot learn the contents of queries and data records during query processing. (2) Efficiency: we utilize hierarchical cubes to encode multi-dimensional data records and construct a secure tree index on top of such encoding to achieve sublinear query time. (3) Verifiability: our scheme allows users to verify the correctness and completeness of the query results to address server's malicious behaviors. We perform formal security analysis and comprehensive experimental evaluations. The results on real datasets demonstrate that our scheme achieves practical performance while guaranteeing data privacy and result integrity.

Computing similarity, especially Jaccard Similarity, between two datasets is a fundamental building block in big data analytics, and extensive applications including genome matching, plagiarism detection, social networking, etc. The increasing user privacy concerns over the release of has sensitive data have made it desirable and necessary for two users to evaluate Jaccard Similarity over their datasets in a privacy-preserving manner. In this paper, we propose two efficient and secure protocols to compute the Jaccard Similarity of two users' private sets with the help of an unfully-trusted server. Specifically, in order to boost the efficiency, we leverage Minhashing algorithm on encrypted data, where the output of our protocols is guaranteed to be a close approximation of the exact value. In both protocols, only an approximate similarity result is leaked to the server and users. The first protocol is secure against a semi-honest server, while the second protocol, with a novel consistency-check mechanism, further achieves result verifiability against a malicious server who cheats in the executions. Experimental results show that our first protocol computes an approximate Jaccard Similarity of two billion-element sets within only 6 minutes (under 256-bit security in parallel mode). To the best of our knowledge, our consistency-check mechanism represents the very first work to realize an efficient verification particularly on approximate similarity computation.