A Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

Performing smart computations in a context of cloud computing and big data is highly appreciated today. Fully homomorphic encryption (FHE) is a smart category of encryption schemes that allows working with the data in its encrypted form. It permits us to preserve confidentiality of our sensible data and to benefit from cloud computing powers. Currently, it has been demonstrated by many existing schemes that the theory is feasible but the efficiency needs to be dramatically improved in order to make it usable for real applications. One subtle difficulty is how to efficiently handle the noise. This paper aims to introduce an efficient and verifiable FHE based on a new mathematic structure that is noise free

A Survey on Homomorphic Encryption Schemes: Theory and Implementation

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.Comment: - Updated. (October 6, 2017) - This paper is an early draft of the survey that is being submitted to ACM CSUR and has been uploaded to arXiv for feedback from stakeholder

A secure data outsourcing scheme based on Asmuth – Bloom secret sharing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data outsourcing is an emerging paradigm for data management in which a database is provided as a service by third-party service providers. One of the major benefits of offering database as a service is to provide organisations, which are unable to purchase expensive hardware and software to host their databases, with efficient data storage accessible online at a cheap rate. Despite that, several issues of data confidentiality, integrity, availability and efficient indexing of users’ queries at the server side have to be addressed in the data outsourcing paradigm. Service providers have to guarantee that their clients’ data are secured against internal (insider) and external attacks. This paper briefly analyses the existing indexing schemes in data outsourcing and highlights their advantages and disadvantages. Then, this paper proposes a secure data outsourcing scheme based on Asmuth–Bloom secret sharing which tries to address the issues in data outsourcing such as data confidentiality, availability and order preservation for efficient indexing

An improved Framework for Biometric Database’s privacy

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language

Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2019, Issue

The Potential for Machine Learning Analysis over Encrypted Data in Cloud-based Clinical Decision Support - Background and Review

This paper appeared at the 8th Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2015), Sydney, Australia, January 2015. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 164, Anthony Maeder and Jim Warren, Ed. Reproduction for academic, not-for profit purposes permitted provided this text is includedIn an effort to reduce the risk of sensitive data exposure in untrusted networks such as the public cloud, increasing attention has recently been given to encryption schemes that allow specific computations to occur on encrypted data, without the need for decryption. This relies on the fact that some encryption algorithms display the property of homomorphism, which allows them to manipulate data in a meaningful way while still in encrypted form. Such a framework would find particular relevance in Clinical Decision Support (CDS) applications deployed in the public cloud. CDS applications have an important computational and analytical role over confidential healthcare information with the aim of supporting decision-making in clinical practice. This review paper examines the history and current status of homomoprhic encryption and its potential for preserving the privacy of patient data underpinning cloud-based CDS applications

Encriptação parcialmente homomórfica CCA1-segura

Orientadores: Ricardo Dahab, Diego de Freitas AranhaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nesta tese nosso tema de pesquisa é a encriptação homomórfica, com foco em uma solução prática e segura para encriptação parcialmente homomórfica (somewhat homomorphic encryption - SHE), considerando o modelo de segurança conhecido como ataque de texto encriptado escolhido (chosen ciphertext attack - CCA). Este modelo pode ser subdividido em duas categorias, a saber, CCA1 e CCA2, sendo CCA2 o mais forte. Sabe-se que é impossível construir métodos de encriptação homomórfica que sejam CCA2-seguros. Por outro lado, é possível obter segurança CCA1, mas apenas um esquema foi proposto até hoje na literatura; assim, seria interessante haver outras construções oferecendo este tipo de segurança. Resumimos os principais resultados desta tese de doutorado em duas contribuições. A primeira é mostrar que a família NTRU de esquemas SHE é vulnerável a ataques de recuperação de chave privada, e portanto não são CCA1-seguros. A segunda é a utilização de computação verificável para obter esquemas SHE que são CCA1-seguros e que podem ser usados para avaliar polinômios multivariáveis quadráticos. Atualmente, métodos de encriptação homomórfica são construídos usando como substrato dois problemas de difícil solução: o MDC aproximado (approximate GCD problem - AGCD) e o problema de aprendizado com erros (learning with errors - LWE). O problema AGCD leva, em geral, a construções mais simples mas com desempenho inferior, enquanto que os esquemas baseados no problema LWE correspondem ao estado da arte nesta área de pesquisa. Recentemente, Cheon e Stehlé demonstraram que ambos problemas estão relacionados, e é uma questão interessante investigar se esquemas baseados no problema AGCD podem ser tão eficientes quanto esquemas baseados no problema LWE. Nós respondemos afirmativamente a esta questão para um cenário específico: estendemos o esquema de computação verificável proposto por Fiore, Gennaro e Pastro, de forma que use a suposição de que o problema AGCD é difícil, juntamente com o esquema DGHV adaptado para uso do Teorema Chinês dos Restos (Chinese remainder theorem - CRT) de forma a evitar ataques de recuperação de chave privadaAbstract: In this thesis we study homomorphic encryption with focus on practical and secure somewhat homomorphic encryption (SHE), under the chosen ciphertext attack (CCA) security model. This model is classified into two different main categories: CCA1 and CCA2, with CCA2 being the strongest. It is known that it is impossible to construct CCA2-secure homomorphic encryption schemes. On the other hand, CCA1-security is possible, but only one scheme is known to achieve it. It would thus be interesting to have other CCA1-secure constructions. The main results of this thesis are summarized in two contributions. The first is to show that the NTRU-family of SHE schemes is vulnerable to key recovery attacks, hence not CCA1-secure. The second is the utilization of verifiable computation to obtain a CCA1-secure SHE scheme that can be used to evaluate quadratic multivariate polynomials. Homomorphic encryption schemes are usually constructed under the assumption that two distinct problems are hard, namely the Approximate GCD (AGCD) Problem and the Learning with Errors (LWE) Problem. The AGCD problem leads, in general, to simpler constructions, but with worse performance, wheras LWE-based schemes correspond to the state-of-the-art in this research area. Recently, Cheon and Stehlé proved that both problems are related, and thus it is an interesting problem to investigate if AGCD-based SHE schemes can be made as efficient as their LWE counterparts. We answer this question positively for a specific scenario, extending the verifiable computation scheme proposed by Fiore, Gennaro and Pastro to work under the AGCD assumption, and using it together with the Chinese Remainder Theorem (CRT)-version of the DGHV scheme, in order to avoid key recovery attacksDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação143484/2011-7CNPQCAPE