From security to assurance in the cloud: a survey

The cloud computing paradigm has become a mainstream solution for the deployment of business processes and applications. In the public cloud vision, infrastructure, platform, and software services are provisioned to tenants (i.e., customers and service providers) on a pay-as-you-go basis. Cloud tenants can use cloud resources at lower prices, and higher performance and flexibility, than traditional on-premises resources, without having to care about infrastructure management. Still, cloud tenants remain concerned with the cloud's level of service and the nonfunctional properties their applications can count on. In the last few years, the research community has been focusing on the nonfunctional aspects of the cloud paradigm, among which cloud security stands out. Several approaches to security have been described and summarized in general surveys on cloud security techniques. The survey in this article focuses on the interface between cloud security and cloud security assurance. First, we provide an overview of the state of the art on cloud security. Then, we introduce the notion of cloud security assurance and analyze its growing impact on cloud security approaches. Finally, we present some recommendations for the development of next-generation cloud security and assurance solutions

Exploiting cloud utility models for profit and ruin

A key characteristic that has led to the early adoption of public cloud computing is the utility pricing model that governs the cost of compute resources consumed. Similar to public utilities like gas and electricity, cloud consumers only pay for the resources they consume and only for the time they are utilized. As a result and pursuant to a Cloud Service Provider\u27s (CSP) Terms of Agreement, cloud consumers are responsible for all computational costs incurred within and in support of their rented computing environments whether these resources were consumed in good faith or not. While initial threat modeling and security research on the public cloud model has primarily focused on the confidentiality and integrity of data transferred, processed, and stored in the cloud, little attention has been paid to the external threat sources that have the capability to affect the financial viability of cloud-hosted services. Bounded by a utility pricing model, Internet-facing web resources hosted in the cloud are vulnerable to Fraudulent Resource Consumption (FRC) attacks. Unlike an application-layer DDoS attack that consumes resources with the goal of disrupting short-term availability, a FRC attack is a considerably more subtle attack that instead targets the utility model over an extended time period. By fraudulently consuming web resources in sufficient volume (i.e. data transferred out of the cloud), an attacker is able to inflict significant fraudulent charges to the victim. This work introduces and thoroughly describes the FRC attack and discusses why current application-layer DDoS mitigation schemes are not applicable to a more subtle attack. The work goes on to propose three detection metrics that together form the criteria for detecting a FRC attack from that of normal web activity and an attribution methodology capable of accurately identifying FRC attack clients. Experimental results based on plausible and challenging attack scenarios show that an attacker, without knowledge of the training web log, has a difficult time mimicking the self-similar and consistent request semantics of normal web activity necessary to carryout a successful FRC attack

Swarm Differential Privacy for Purpose Driven Data-Information-Knowledge-Wisdom Architecture

Privacy protection has recently been in the spotlight of attention to both academia and industry. Society protects individual data privacy through complex legal frameworks. The increasing number of applications of data science and artificial intelligence has resulted in a higher demand for the ubiquitous application of the data. The privacy protection of the broad Data-Information-Knowledge-Wisdom (DIKW) landscape, the next generation of information organization, has taken a secondary role. In this paper, we will explore DIKW architecture through the applications of the popular swarm intelligence and differential privacy. As differential privacy proved to be an effective data privacy approach, we will look at it from a DIKW domain perspective. Swarm Intelligence can effectively optimize and reduce the number of items in DIKW used in differential privacy, thus accelerating both the effectiveness and the efficiency of differential privacy for crossing multiple modals of conceptual DIKW. The proposed approach is demonstrated through the application of personalized data that is based on the open-sourse IRIS dataset. This experiment demonstrates the efficiency of Swarm Intelligence in reducing computing complexity

DA-Encrypt: Homomorphic Encryption via Non-Archimedean Diophantine Approximation --- Preliminary Report

We give a theoretical description of a new homomorphic encryption scheme DA-Encrypt that is based on (non-archimedean) Diophantine Approximation

A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks

Machine learning (ML) is widely used today, especially through deep neural networks (DNNs); however, increasing computational load and resource requirements have led to cloud-based solutions. To address this problem, a new generation of networks has emerged called spiking neural networks (SNNs), which mimic the behavior of the human brain to improve efficiency and reduce energy consumption. These networks often process large amounts of sensitive information, such as confidential data, and thus privacy issues arise. Homomorphic encryption (HE) offers a solution, allowing calculations to be performed on encrypted data without decrypting them. This research compares traditional DNNs and SNNs using the Brakerski/Fan-Vercauteren (BFV) encryption scheme. The LeNet-5 and AlexNet models, widely-used convolutional architectures, are used for both DNN and SNN models based on their respective architectures, and the networks are trained and compared using the FashionMNIST dataset. The results show that SNNs using HE achieve up to 40% higher accuracy than DNNs for low values of the plaintext modulus t, although their execution time is longer due to their time-coding nature with multiple time steps

Gestion de la Sécurité pour le Cyber-Espace - Du Monitorage Intelligent à la Configuration Automatique

The Internet has become a great integration platform capable of efficiently interconnecting billions of entities, from simple sensors to large data centers. This platform provides access to multiple hardware and virtualized resources (servers, networking, storage, applications, connected objects) ranging from cloud computing to Internet-of-Things infrastructures. From these resources that may be hosted and distributed amongst different providers and tenants, the building and operation of complex and value-added networked systems is enabled. These systems arehowever exposed to a large variety of security attacks, that are also gaining in sophistication and coordination. In that context, the objective of my research work is to support security management for the cyberspace, with the elaboration of new monitoring and configuration solutionsfor these systems. A first axis of this work has focused on the investigation of smart monitoring methods capable to cope with low-resource networks. In particular, we have proposed a lightweight monitoring architecture for detecting security attacks in low-power and lossy net-works, by exploiting different features provided by a routing protocol specifically developed for them. A second axis has concerned the assessment and remediation of vulnerabilities that may occur when changes are operated on system configurations. Using standardized vulnerability descriptions, we have designed and implemented dedicated strategies for improving the coverage and efficiency of vulnerability assessment activities based on versioning and probabilistic techniques, and for preventing the occurrence of new configuration vulnerabilities during remediation operations. A third axis has been dedicated to the automated configuration of virtualized resources to support security management. In particular, we have introduced a software-defined security approach for configuring cloud infrastructures, and have analyzed to what extent programmability facilities can contribute to their protection at the earliest stage, through the dynamic generation of specialized system images that are characterized by low attack surfaces. Complementarily, we have worked on building and verification techniques for supporting the orchestration of security chains, that are composed of virtualized network functions, such as firewalls or intrusion detection systems. Finally, several research perspectives on security automation are pointed out with respect to ensemble methods, composite services and verified artificial intelligence.L’Internet est devenu une formidable plateforme d’intégration capable d’interconnecter efficacement des milliards d’entités, de simples capteurs à de grands centres de données. Cette plateforme fournit un accès à de multiples ressources physiques ou virtuelles, allant des infra-structures cloud à l’internet des objets. Il est possible de construire et d’opérer des systèmes complexes et à valeur ajoutée à partir de ces ressources, qui peuvent être déployées auprès de différents fournisseurs. Ces systèmes sont cependant exposés à une grande variété d’attaques qui sont de plus en plus sophistiquées. Dans ce contexte, l’objectif de mes travaux de recherche porte sur une meilleure gestion de la sécurité pour le cyberespace, avec l’élaboration de nouvelles solutions de monitorage et de configuration pour ces systèmes. Un premier axe de ce travail s’est focalisé sur l’investigation de méthodes de monitorage capables de répondre aux exigences de réseaux à faibles ressources. En particulier, nous avons proposé une architecture de surveillance adaptée à la détection d’attaques dans les réseaux à faible puissance et à fort taux de perte, en exploitant différentes fonctionnalités fournies par un protocole de routage spécifiquement développépour ceux-ci. Un second axe a ensuite concerné la détection et le traitement des vulnérabilités pouvant survenir lorsque des changements sont opérés sur la configuration de tels systèmes. En s’appuyant sur des bases de descriptions de vulnérabilités, nous avons conçu et mis en œuvre différentes stratégies permettant d’améliorer la couverture et l’efficacité des activités de détection des vulnérabilités, et de prévenir l’occurrence de nouvelles vulnérabilités lors des activités de traitement. Un troisième axe fut consacré à la configuration automatique de ressources virtuelles pour la gestion de la sécurité. En particulier, nous avons introduit une approche de programmabilité de la sécurité pour les infrastructures cloud, et avons analysé dans quelle mesure celle-ci contribue à une protection au plus tôt des ressources, à travers la génération dynamique d’images systèmes spécialisées ayant une faible surface d’attaques. De façon complémentaire, nous avonstravaillé sur des techniques de construction automatique et de vérification de chaînes de sécurité, qui sont composées de fonctions réseaux virtuelles telles que pare-feux ou systèmes de détection d’intrusion. Enfin, plusieurs perspectives de recherche relatives à la sécurité autonome sont mises en évidence concernant l’usage de méthodes ensemblistes, la composition de services, et la vérification de techniques d’intelligence artificielle

Fast and Secure Linear Regression and Biometric Authentication with Security Update

We explicitly present a homomorphic encryption scheme with a flexible encoding of plaintexts. We prove its security under the LWE assumption, and innovatively show how the scheme can be used to handle computations over both binary strings and real numbers. In addition, using the scheme and its features, we build fast and secure systems of - linear regression using gradient descent, namely finding a reasonable linear relation between data items which remain encrypted. Compared to the best previous work over a simulated dataset of $10^8$ records each with 20 features, our system dramatically reduces the server running time from about 8.75 hours (of the previous work) to only about 10 minutes. - biometric authentication, in which we show how to reduce ciphertext sizes by half and to do the computation at the server very fast, compared with the state-of-the-art. Moreover, as key rotation is a vital task in practice and is recommended by many authorized organizations for key management, - we show how to do key rotation over encrypted data, without any decryption involved, and yet homomorphic properties of ciphertexts remain unchanged. In addition, our method of doing key rotation handles keys of different security levels (e.g., 80- and 128-bit securities), so that the security of ciphertexts and keys in our scheme can be updated , namely can be changed into a higher security level

Secure Testing for Genetic Diseases on Encrypted Genomes with Homomorphic Encryption Scheme

The decline in genome sequencing costs has widened the population that can afford its cost and has also raised concerns about genetic privacy. Kim et al. present a practical solution to the scenario of secure searching of gene data on a semitrusted business cloud. However, there are three errors in their scheme. We have made three improvements to solve these three errors. (1) They truncate the variation encodings of gene to 21 bits, which causes LPCE error and more than 5% of the entries in the database cannot be queried integrally. We decompose these large encodings by 44 bits and deal with the components, respectively, to avoid LPCE error. (2) We abandon the hash function used in Kim’s scheme, which may cause HCE error with a probability of 2-22 and decompose the position encoding of gene into three parts with the basis 211 to avoid HCE error. (3) We analyze the relationship between the parameters and the CCE error and specify the condition that parameters need to satisfy to avoid the CCE error. Experiments show that our scheme can search all entries, and the probability of searching error is reduced to less than 2-37.4