A secure privacy preserving deduplication scheme for cloud computing

© 2019 Elsevier B.V. Data deduplication is a key technique to improve storage efficiency in cloud computing. By pointing redundant files to a single copy, cloud service providers greatly reduce their storage space as well as data transfer costs. Despite of the fact that the traditional deduplication approach has been adopted widely, it comes with a high risk of losing data confidentiality because of the data storage models in cloud computing. To deal with this issue in cloud storage, we first propose a TEE (trusted execution environment) based secure deduplication scheme. In our scheme, each cloud user is assigned a privilege set; the deduplication can be performed if and only if the cloud users have the correct privilege. Moreover, our scheme augments the convergent encryption with users’ privileges and relies on TEE to provide secure key management, which improves the ability of such cryptosystem to resist chosen plaintext attacks and chosen ciphertext attacks. A security analysis indicates that our scheme is secure enough to support data deduplication and to protect the confidentiality of sensitive data. Furthermore, we implement a prototype of our scheme and evaluate the performance of our prototype, experiments show that the overhead of our scheme is practical in realistic environments

ase-PoW: a proof of ownership mechanism for cloud deduplication in hierarchical environments

Proof-of-Ownership (PoW) can be an efective deduplication technique to reduce storage requirements, by providing cloud storage servers the capability to guarantee that clients only upload and download files that they are in possession of. In this paper, we propose an attribute symmetric encryption PoW scheme (ase-PoW) for hierarchical environments such as corporations, in which (1) the external cloud service provider is honest-but-curious and (2) there is a exible access control in place to ensure only users with the right privilege can access sensitive files. This is, to the best of our knowledge, the first such scheme and it is built upon the ce-PoW scheme of Gonzalez-Manzano and Orfila (2015). Ase-PoW outperforms ce-PoW in that it does not suffer from content-guessing attacks, it reduces client storage needs and computational workload.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 CIBERDINE-CM (CIBERDINE: Cybersecurity, Data, and Risks) funded by Madrid Autonomous Community and co-funded by European funds. L. Gonzalez and J. M. de Fuentes were also supported by the Programa de Ayudas para la Movilidad of Carlos III University of Madrid, Spain

A software approach to defeating side channels in last-level caches

We present a software approach to mitigate access-driven side-channel attacks that leverage last-level caches (LLCs) shared across cores to leak information between security domains (e.g., tenants in a cloud). Our approach dynamically manages physical memory pages shared between security domains to disable sharing of LLC lines, thus preventing "Flush-Reload" side channels via LLCs. It also manages cacheability of memory pages to thwart cross-tenant "Prime-Probe" attacks in LLCs. We have implemented our approach as a memory management subsystem called CacheBar within the Linux kernel to intervene on such side channels across container boundaries, as containers are a common method for enforcing tenant isolation in Platform-as-a-Service (PaaS) clouds. Through formal verification, principled analysis, and empirical evaluation, we show that CacheBar achieves strong security with small performance overheads for PaaS workloads

Blockchain-based Cloud Data Deduplication Scheme with Fair Incentives

With the rapid development of cloud computing, vast amounts of duplicated data are being uploaded to the cloud, wasting storage resources. Deduplication (dedup) is an efficient solution to save storage costs of cloud storage providers (CSPs) by storing only one copy of the uploaded data. However, cloud users do not benefit directly from dedup and may be reluctant to dedup their data. To motivate the cloud users towards dedup, CSPs offer incentives on storage fees. The problems with the existing dedup schemes are that they do not consider: (1) correctness - the incentive offered to a cloud user should be computed correctly without any prejudice. (2) fairness - the cloud user receives the file link and access rights of the uploaded data if and only if the CSP receives the storage fee. Meeting these requirements without a trusted party is non-trivial, and most of the existing dedup schemes do not apply. Another drawback is that most of the existing schemes emphasize incentives to cloud users but failed to provide a reliable incentive mechanism. As public Blockchain networks emulate the properties of trusted parties, in this paper, we propose a new Blockchain-based dedup scheme to meet the above requirements. In our scheme, a smart contract computes the incentives on storage fee, and the fairness rules are encoded into the smart contract for facilitating fair payments between the CSPs and cloud users. We prove the correctness and fairness of the proposed scheme. We also design a new incentive mechanism and show that the scheme is individually rational and incentive compatible. Furthermore, we conduct experiments by implementing the designed smart contract on Ethereum local Blockchain network and list the transactional and financial costs of interacting with the designed smart contract

Um estudo sobre a segurança e privacidade no armazenamento de dados em nuvens

Orientador: Marco Aurélio Amaral HenriquesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Armazenamento de dados na nuvem é um serviço que traz diversas vantagens aos seus usuários. Contudo, em sistemas de nuvens públicas, os riscos envolvidos na terceirização do armazenamento de dados pode ser uma barreira para a adoção deste serviço por aqueles preocupados com sua privacidade. Vários provedores de serviços em nuvem que afirmam proteger os dados do usuário não atendem alguns requisitos considerados essenciais em um serviço seguro, confiável e de fácil utilização, levantando questionamentos sobre a segurança efetivamente obtida. Apresentamos neste trabalho um estudo relacionado aos requisitos de privacidade dos usuários e de segurança de seus dados em nuvens públicas. O estudo apresenta algumas técnicas normalmente usadas para atender tais requisitos, juntamente com uma análise de seus benefícios e custos relativos. Além disso, ele faz uma avaliação destes requisitos em vários sistemas de nuvens públicas. Depois de comparar estes sistemas, propomos um conjunto de requisitos e apresentamos, como prova de conceito, uma aplicação baseada nos mesmos, a qual melhora a segurança dos dados e a privacidade dos usuários. Nós mostramos que é possível proteger os dados armazenados nas nuvens contra o acesso por terceiros (incluindo os administradores das nuvens) sem sobrecarregar o usuário com protocolos ou procedimentos complexos de segurança, tornando o serviço de armazenamento em nuvens uma escolha mais confiável para usuários preocupados com sua privacidadeAbstract: Cloud data storage is a service that brings several advantages for its users. However, in public cloud systems, the risks involved in the outsourcing of data storage can be a barrier to the adoption of this service by those concerned with privacy. Several cloud service providers that claim to protect user's data do not fulfill some requirements considered essential in a secure, reliable and easy to use service, raising questions about the effective security obtained. We present here a study related to user's privacy and data security requirements on public clouds. The study presents some techniques normally used to fulfill those requirements, along with an analysis of their relative costs and benefits. Moreover, it makes an evaluation of them in several public cloud systems. After comparing those systems, we propose a set of requirements and present a proof of concept application based on them, which improves data security and user privacy in public clouds. We show that it is possible to protect cloud stored data against third party (including cloud administrators) access without burdening the user with complex security protocols or procedures, making the public cloud storage service a more reliable choice to privacy concerned usersMestradoEngenharia de ComputaçãoMestre em Engenharia Elétrica153392/2014-2CNP

An Info-Leak Resistant Kernel Randomization

[EN] Given the significance that the cloud paradigm has in modern society, it is extremely important to provide security to users at all levels, especially at the most fundamental ones since these are the most sensitive and potentially harmful in the event of an attack. However, the cloud computing paradigm brings new challenges in which security mechanisms are weakened or deactivated to improve profitability and exploitation of the available resources. Kernel randomization is an important security mechanism that is currently present in all main operating systems. Function-Granular Kernel Randomization is a new step that aims to be the future of the kernel randomization, because it provides much more security than current kernel randomization approaches. Unfortunately, function-granular kernel randomization also impacts significantly on the performance and potential benefits of memory deduplication. Both function-granular kernel randomization and memory deduplication are desired and beneficial; the first for the strong protection it gives, and the second for the reduction of costs in terms of memory consumption. In this paper, we analyse the impact of function-granular kernel randomization on memory deduplication revealing why it cannot offer maximum security and shareability of memory simultaneously. We also discuss the reasons why having a full position independent kernel code counter-intuitively does not solve the problem introducing a challenge to kernel randomization designers. To solve these problems, we propose a function-granular kernel randomization modification for cloud systems that enables full function-granular kernel randomization while reduces memory deduplication cancellations to almost zero. The proposed approach forces guest kernels of the same tenant to have the same random memory layout of memory regions with high impact on deduplication, ensuring a high rate of deduplicated pages while the kernel randomization is fully enabled. Our approach enables cloud providers to have both, high levels of security and an efficient use of resources.Vañó-García, F.; Marco-Gisbert, H. (2020). An Info-Leak Resistant Kernel Randomization. IEEE Access. 8:161612-161629. https://doi.org/10.1109/ACCESS.2020.3019774S161612161629

Efficient, Dependable Storage of Human Genome Sequencing Data

A compreensão do genoma humano impacta várias áreas da vida. Os dados oriundos do genoma humano são enormes pois existem milhões de amostras a espera de serem sequenciadas e cada genoma humano sequenciado pode ocupar centenas de gigabytes de espaço de armazenamento. Os genomas humanos são críticos porque são extremamente valiosos para a investigação e porque podem fornecer informações delicadas sobre o estado de saúde dos indivíduos, identificar os seus dadores ou até mesmo revelar informações sobre os parentes destes. O tamanho e a criticidade destes genomas, para além da quantidade de dados produzidos por instituições médicas e de ciências da vida, exigem que os sistemas informáticos sejam escaláveis, ao mesmo tempo que sejam seguros, confiáveis, auditáveis e com custos acessíveis. As infraestruturas de armazenamento existentes são tão caras que não nos permitem ignorar a eficiência de custos no armazenamento de genomas humanos, assim como em geral estas não possuem o conhecimento e os mecanismos adequados para proteger a privacidade dos dadores de amostras biológicas. Esta tese propõe um sistema de armazenamento de genomas humanos eficiente, seguro e auditável para instituições médicas e de ciências da vida. Ele aprimora os ecossistemas de armazenamento tradicionais com técnicas de privacidade, redução do tamanho dos dados e auditabilidade a fim de permitir o uso eficiente e confiável de infraestruturas públicas de computação em nuvem para armazenar genomas humanos. As contribuições desta tese incluem (1) um estudo sobre a sensibilidade à privacidade dos genomas humanos; (2) um método para detetar sistematicamente as porções dos genomas que são sensíveis à privacidade; (3) algoritmos de redução do tamanho de dados, especializados para dados de genomas sequenciados; (4) um esquema de auditoria independente para armazenamento disperso e seguro de dados; e (5) um fluxo de armazenamento completo que obtém garantias razoáveis de proteção, segurança e confiabilidade a custos modestos (por exemplo, menos de $1/Genoma/Ano), integrando os mecanismos propostos a configurações de armazenamento apropriadasThe understanding of human genome impacts several areas of human life. Data from human genomes is massive because there are millions of samples to be sequenced, and each sequenced human genome may size hundreds of gigabytes. Human genomes are critical because they are extremely valuable to research and may provide hints on individuals’ health status, identify their donors, or reveal information about donors’ relatives. Their size and criticality, plus the amount of data being produced by medical and life-sciences institutions, require systems to scale while being secure, dependable, auditable, and affordable. Current storage infrastructures are too expensive to ignore cost efficiency in storing human genomes, and they lack the proper knowledge and mechanisms to protect the privacy of sample donors. This thesis proposes an efficient storage system for human genomes that medical and lifesciences institutions may trust and afford. It enhances traditional storage ecosystems with privacy-aware, data-reduction, and auditability techniques to enable the efficient, dependable use of multi-tenant infrastructures to store human genomes. Contributions from this thesis include (1) a study on the privacy-sensitivity of human genomes; (2) to detect genomes’ privacy-sensitive portions systematically; (3) specialised data reduction algorithms for sequencing data; (4) an independent auditability scheme for secure dispersed storage; and (5) a complete storage pipeline that obtains reasonable privacy protection, security, and dependability guarantees at modest costs (e.g., less than$1/Genome/Year) by integrating the proposed mechanisms with appropriate storage configurations