In this paper we define the overflow problem of a network coding storage system in which the encoding parameter and the storage parameter are mismatched. Through analyses and experiments, we first show the impacts of the overflow problem in a network coding scheme, which not only waste storage spaces, but also degrade coding efficiency. To avoid the overflow problem, we then develop the network coding based secure storage (NCSS) scheme. Thanks to considering both security and storage requirements in encoding procedures and distributed architectures, the NCSS can improve the performance of a cloud storage system from both the aspects of storage cost and coding processing time. We analyze the maximum allowable stored encoded data under the perfect secrecy criterion, and provide the design guidelines for the secure cloud storage system to enhance coding efficiency and achieve the minimal storage cost.
For secure storage over multiple clouds, similar to this work, the authors of @cite_26 proposed a security protection scheme to ensure that no symbols can be decoded by an eavesdropper, which is weaker than perfect secrecy. In @cite_4 , a link eavesdropping problem in a network-coded cloud storage system was investigated. A publicly verifiable protocol for network coded cloud storage was proposed in @cite_11 .
@cite_26 @cite_4 @cite_11
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This paper reveals an intrinsic relationship between secure cloud storage and secure network coding for the first time. Secure cloud storage was proposed only recently while secure network coding has been studied for more than ten years. Although the two areas are quite different in their nature and are studied independently, we show how to construct a secure cloud storage protocol given any secure network coding protocol. This gives rise to a systematic way to construct secure cloud storage protocols. Our construction is secure under a definition which captures the real world usage of the cloud storage. Furthermore, we propose two specific secure cloud storage protocols based on two recent secure network coding protocols. In particular, we obtain the first publicly verifiable secure cloud storage protocol in the standard model. We also enhance the proposed generic construction to support user anonymity and third-party public auditing, which both have received considerable attention recently. Finally, we prototype the newly proposed protocol and evaluate its performance. Experimental results validate the effectiveness of the protocol. We focus on the problem of secure distributed storage over multiple untrusted clouds or networks. Our main contribution is a low complexity scheme that relies on erasure coding techniques for achieving prescribed levels of confidentiality and reliability. Using matrices that have no singular square submatrices, we subject the original data to a linear transformation. The resulting coded symbols are then stored in different networks. This scheme allows users with access to a threshold number of networks to reconstruct perfectly the original data, while ensuring that eavesdroppers with access to any number of networks smaller than this threshold are unable to decode any of the original symbols. This holds even if the attackers are able to guess some of the missing symbols. We further quantify the achievable level of security, and analyze the complexity of the proposed scheme. We consider distributed storage over two untrusted networks, whereby coding is used as a means to achieve a prescribed level of confidentiality. The key idea is to exploit the algebraic structure of the Vandermonde matrix to mix the input blocks, before they are stored in different locations. The proposed scheme ensures that eavesdroppers with access to only one of the networks are unable to decode any symbol even if they are capable of guessing some of the missing blocks. Information-theoretic techniques allow us to quantify the achievable level of confidentiality. Moreover, the proposed approach is shown to offer low complexity and optimal rate. As Cloud Computing becomes prevalent, sensitive information are being increasingly centralized into the cloud. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow users to securely search over encrypted data through keywords, these techniques support only boolean search, without capturing any relevance of data files. This approach suffers from two main drawbacks when directly applied in the context of Cloud Computing. On the one hand, users, who do not necessarily have pre-knowledge of the encrypted cloud data, have to post process every retrieved file in order to find ones most matching their interest, On the other hand, invariably retrieving all files containing the queried keyword further incurs unnecessary network traffic, which is absolutely undesirable in today's pay-as-you-use cloud paradigm. In this paper, for the first time we define and solve the problem of effective yet secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer towards practical deployment of privacy-preserving data hosting services in Cloud Computing. We first give a straightforward yet ideal construction of ranked keyword search under the state-of-the-art searchable symmetric encryption (SSE) security definition, and demonstrate its inefficiency. To achieve more practical performance, we then propose a definition for ranked searchable symmetric encryption, and give an efficient design by properly utilizing the existing cryptographic primitive, order-preserving symmetric encryption (OPSE). Thorough analysis shows that our proposed solution enjoys as-strong-as-possible" security guarantee compared to previous SSE schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution. We consider the problem of secure distributed data storage under the paradigm of , in which no is leaked to the eavesdropper. More specifically, the eavesdropper cannot get any information about any individual message file or a small group of files. The key benefit of the weak security paradigm is that it incurs no loss in the storage capacity, which makes it practically appealing. In this paper, we present a coding scheme, using a coset coding based outer code and a Product-Matrix Minimum Bandwidth Regenerating code (proposed by ) as an inner code, that achieves weak security when the eavesdropper can observe any single storage node. We show that the proposed construction has good security properties and requires small finite field size. We address the problem of securing distributed storage systems against passive eavesdroppers that can observe a limited number of storage nodes. An important aspect of these systems is node failures over time, which demand a repair mechanism aimed at maintaining a targeted high level of system reliability. If an eavesdropper observes a node that is added to the system to replace a failed node, it will have access to all the data downloaded during repair, which can potentially compromise the entire information in the system.We are interested in determining the secrecy capacity of distributed storage systems under repair dynamics, i.e., the maximum amount of data that can be securely stored and made available to a legitimate user without revealing any information to any eavesdropper. We derive a general upper bound on the secrecy capacity and show that this bound is tight for the bandwidth-limited regime which is of importance in scenarios such as peer-to-peer distributed storage systems. We also provide a simple explicit code construction that achieves the capacity for this regime. This paper addresses the issue of securing information stored on a distributed storage system from a passive eavesdropping attack. The security notion is perfect secrecy, i.e., the system is said to be secure only if the mutual information between the stored information and the observations at the adversary is zero. The paper summarizes state of the art on securing repair-efficient distributed storage systems. Then, storage systems that employ locally repairable codes with minimum bandwidth regenerating codes as local codes (MBR-LRCs) are investigated. A secure file size upper bound and a construction of secure MBR-LRCs are provided. These two are shown to match under special cases, establishing the secrecy capacity of these systems. Searchable (symmetric) encryption allows encryption while still enabling search for keywords. Its immediate application is cloud storage where a client outsources its files while the (cloud) service provider should search and selectively retrieve those. Searchable encryption is an active area of research and a number of schemes with different efficiency and security characteristics have been proposed in the literature. Any scheme for practical adoption should be efficient -- i.e. have sub-linear search time --, dynamic -- i.e. allow updates -- and semantically secure to the most possible extent. Unfortunately, efficient, dynamic searchable encryption schemes suffer from various drawbacks. Either they deteriorate from semantic security to the security of deterministic encryption under updates, they require to store information on the client and for deleted files and keywords or they have very large index sizes. All of this is a problem, since we can expect the majority of data to be later added or changed. Since these schemes are also less efficient than deterministic encryption, they are currently an unfavorable choice for encryption in the cloud. In this paper we present the first searchable encryption scheme whose updates leak no more information than the access pattern, that still has asymptotically optimal search time, linear, very small and asymptotically optimal index size and can be implemented without storage on the client (except the key). Our construction is based on the novel idea of learning the index for efficient access from the access pattern itself. Furthermore, we implement our system and show that it is highly efficient for cloud storage. Users are increasingly storing, accessing, and exchanging data through public cloud services such as those provided by Google, Facebook, Apple, and Microsoft. Although users may want to have faith in cloud providers to provide good security protection, the confidentiality of any data in public clouds can be violated, and consequently, while providers may not be "doing evil," we can not and should not trust them with data confidentiality. To better protect the privacy of user data stored in the cloud, in this paper we propose a privacy-preserving system called Mimesis Aegis (M-Aegis) that is suitable for mobile platforms. M-Aegis is a new approach to user data privacy that not only provides isolation but also preserves the user experience through the creation of a conceptual layer called Layer 7.5 (L-7.5), which is interposed between the application (OSI Layer 7) and the user (Layer 8). This approach allows M-Aegis to implement true end-to-end encryption of user data with three goals in mind: 1) complete data and logic isolation from untrusted entities; 2) the preservation of original user experience with target apps; and 3) applicable to a large number of apps and resilient to app updates. In order to preserve the exact application workflow and look-and-feel, M-Aegis uses L-7.5 to put a transparent window on top of existing application GUIs to both intercept plaintext user input before transforming the input and feeding it to the underlying app, and to reverse-transform the output data from the app before displaying the plaintext data to the user. This technique allows M-Aegis to transparently integrate with most cloud services without hindering usability and without the need for reverse engineering. We implemented a prototype of M-Aegis on Android and show that it can support a number of popular cloud services, e.g. Gmail, Facebook Messenger, WhatsApp, etc. Our performance evaluation and user study show that users incur minimal overhead when adopting M-Aegis on Android: imperceptible encryption decryption latency and a low and adjustable false positive rate when searching over encrypted data. We consider a problem where multiple agents participate in solving a quadratic optimization problem subject to linear inequality constraints in a privacy-preserving manner. Several variables of the objective function as well as the constraints are privacy-sensitive and are known to different agents. We propose a privacy-preserving protocol based on partially homomorphic encryption where each agent encrypts its own information before sending it to an untrusted cloud computing infrastructure. To find the optimal solution the cloud applies a gradient descent algorithm on the encrypted data without the ability to decrypt it. The privacy of the proposed protocol against coalitions of colluding agents is analyzed using the cryptography notion of zero knowledge proofs. In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multiple-input multiple-output systems with a multi-antenna active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform toward the eavesdropper, thus increasing its received signal power. Assuming matched filter precoding and artificial noise (AN) generation at the transmitter, we derive an asymptotic achievable secrecy rate when the number of transmit antennas approaches infinity. For the case of a single-antenna active eavesdropper, we obtain a closed-form expression for the optimal power allocation policy for the transmit signal and the AN, and find the minimum transmit power required to ensure reliable secure communication. Furthermore, we show that the transmit antenna correlation diversity of the intended users and the eavesdropper can be exploited in order to improve the secrecy rate. In fact, under certain orthogonality conditions of the channel covariance matrices, the secrecy rate loss introduced by the eavesdropper can be completely mitigated. Cloud computing is growing exponentially, whereby there are now hundreds of cloud service providers (CSPs) of various sizes. While the cloud consumers may enjoy cheaper data storage and computation offered in this multi-cloud environment, they are also in face of more complicated reliability issues and privacy preservation problems of their outsourced data. Though searchable encryption allows users to encrypt their stored data while preserving some search capabilities, few efforts have sought to consider the reliability of the searchable encrypted data outsourced to the clouds. In this paper, we propose a privacy-preserving STorage and REtrieval (STRE) mechanism that not only ensures security and privacy but also provides reliability guarantees for the outsourced searchable encrypted data. The STRE mechanism enables the cloud users to distribute and search their encrypted data across multiple independent clouds managed by different CSPs, and is robust even when a certain number of CSPs crash. Besides the reliability, STRE also offers the benefit of partially hidden search pattern. We evaluate the STRE mechanism on Amazon EC2 using a real world dataset and the results demonstrate both effectiveness and efficiency of our approach. Network coding is an important cloud storage technique, which can recover data with small repair bandwidth and high reliability compared to the existing erasure coding and replication methods. However, regardless of which data recovery technique is used, the repaired data in a geographically distributed cloud storage system are easy to be eavesdropped at the transmission link between the local datacenter and its remote backup site. This kind of network security issue is called link eavesdropping in this paper. For a network coded cloud storage system, we propose a systematic design methodology to determine the important data recovery system parameters for any specified security level. Through analysis, we present the performance curves to relate the remote repair bandwidth and the number of coded data fragments. Consequently, all the important system parameters of a network coded data recovery system, including the number of storage nodes and the link capacity between the datacenter and the backup site, can be precisely designed for satisfying different security level requirements. Secure communications can be impeded by eavesdroppers in conventional relay systems. This paper proposes cooperative jamming strategies for two-hop relay networks where the eavesdropper can wiretap the relay channels in both hops. In these approaches, the normally inactive nodes in the relay network can be used as cooperative jamming sources to confuse the eavesdropper. Linear precoding schemes are investigated for two scenarios where single or multiple data streams are transmitted via a decode-and-forward (DF) relay, under the assumption that global channel state information (CSI) is available. For the case of single data stream transmission, we derive closed-form jamming beamformers and the corresponding optimal power allocation. Generalized singular value decomposition (GSVD)-based secure relaying schemes are proposed for the transmission of multiple data streams. The optimal power allocation is found for the GSVD relaying scheme via geometric programming. Based on this result, a GSVD-based cooperative jamming scheme is proposed that shows significant improvement in terms of secrecy rate compared to the approach without jamming. Furthermore, the case involving an eavesdropper with unknown CSI is also investigated in this paper. Simulation results show that the secrecy rate is dramatically increased when inactive nodes in the relay network participate in cooperative jamming. This paper aims to go beyond resilience into the study of security and local-repairability for distributed storage systems (DSSs). Security and local-repairability are both important as features of an efficient storage system, and this paper aims to understand the trade-offs between resilience, security, and local-repairability in these systems. In particular, this paper first investigates security in the presence of colluding eavesdroppers, where eavesdroppers are assumed to work together in decoding the stored information. Second, this paper focuses on coding schemes that enable optimal local repairs. It further brings these two concepts together to develop locally repairable coding schemes for DSS that are secure against eavesdroppers. The main results of this paper include: 1) an improved bound on the secrecy capacity for minimum storage regenerating codes; 2) secure coding schemes that achieve the bound for some special cases; 3) a new bound on minimum distance for locally repairable codes; 4) code construction for locally repairable codes that attain the minimum distance bound; and 5) repair-bandwidth-efficient locally repairable codes with and without security constraints. This is a study of the security of the Coherent One-Way (COW) protocol for quantumcryptography, proposed recently as a simple and fast experimental scheme. In thezero-error regime, the eavesdropper Eve can only take advantage of the losses in thetransmission. We consider new attacks, based on unambiguous state discrimination,which perform better than the basic beam-splitting attack, but which can be detectedby a careful analysis of the detection statistics. These results stress the importance oftesting several statistical parameters in order to achieve higher rates of secret bits. This paper describes a cryptographic protocol for securing self-organized data storage through periodic verifications. The proposed verification protocol, which goes beyond simple integrity checks and proves data conservation, is deterministic, efficient, and scalable. The security of this scheme relies both on the ECDLP intractability assumption and on the difficulty of finding the order of some specific elliptic curve over Zn. The protocol also makes it possible to personalize replicas and to delegate verification without revealing any secret information. We address the problem of securing distributed storage systems against eavesdropping and adversarial attacks. An important aspect of these systems is node failures over time, necessitating, thus, a repair mechanism in order to maintain a desired high system reliability. In such dynamic settings, an important security problem is to safeguard the system from an intruder who may come at different time instances during the lifetime of the storage system to observe and possibly alter the data stored on some nodes. In this scenario, we give upper bounds on the maximum amount of information that can be stored safely on the system. For an important operating regime of the distributed storage system, which we call the bandwidth-limited regime, we show that our upper bounds are tight and provide explicit code constructions. Moreover, we provide a way to short list the malicious nodes and expurgate the system. Cloud storage services enable users to remotely access data in a cloud anytime and anywhere, using any device, in a pay-as-you-go manner. Moving data into a cloud offers great convenience to users since they do not have to care about the large capital investment in both the deployment and management of the hardware infrastructures. However, allowing a cloud service provider (CSP), whose purpose is mainly for making a profit, to take the custody of sensitive data, raises underlying security and privacy issues. To keep user data confidential against an untrusted CSP, a natural way is to apply cryptographic approaches, by disclosing the data decryption key only to authorized users. However, when a user wants to retrieve files containing certain keywords using a thin client, the adopted encryption system should not only support keyword searching over encrypted data, but also provide high performance. In this paper, we investigate the characteristics of cloud storage services and propose a secure and privacy preserving keyword searching (SPKS) scheme, which allows the CSP to participate in the decipherment, and to return only files containing certain keywords specified by the users, so as to reduce both the computational and communication overhead in decryption for users, on the condition of preserving user data privacy and user querying privacy. Performance analysis shows that the SPKS scheme is applicable to a cloud environment. With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE).We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.