As an extension of cloud computing, edge computing introduces additional intermediate devices, called edge nodes near clients, providing computing services on behalf of the central cloud more efficiently. Although edge computing brings several benefits such as low latency and bandwidth savings on the edge side, rapid increase in the amount of data transmitted to the central cloud hinders efficient utilization of the storage system on the central cloud side especially when the data from edge devices are encrypted. To mitigate this issue in a privacy-preserving manner, data deduplication techniques for encrypted data have been extensively studied to enhance both the security and efficiency in the conventional cloud system with two different approaches. A server-side secure deduplication approach protects data privacy but impairs network efficiency by allowing duplicate uploads, while a client-side one improves network efficiency but suffers from potential information leakage due to its vulnerability to the side-channel attack. In this article, we propose a hybrid secure deduplication scheme for edge computing, which guarantees both advantages of the aforementioned two approaches. Specifically, our scheme guarantees data privacy by applying the server-side deduplication technique between the client and the edge nodes and maximizes network efficiency through the client-side deduplication technique between the edge nodes and the cloud. In addition, we devise a novel additively homomorphic encryption for efficient deduplication operations in the resource-limited edge nodes. Based on our experimental results, the proposed scheme reduces the communication costs by approximately 2.5 times for a storage server when the duplicate ratio is 50%, and the response time is reduced by about 2 times when the data size is 16 MB.

Network function virtualization takes advantage of virtualization technology to achieve flexibility in network service provisioning. However, it comes at the cost of security risks caused by cache side-channel attacks on virtual machines. In this study, we investigate the security impact of these attacks on virtualized network functions. In particular, we propose a novel cache-based reconnaissance technique against virtualized Linux-based firewalls. The proposed technique has significant advantages in the perspective of attackers. First, it enhances evasiveness against intrusion detection owing to the ability of source spoofing. Second, it allows inference on a wide variety of filtering rules. During experiment in VyOS, the proposed method could infer the firewall rules with an accuracy of more than 90% by using only a few dozen packets. We also present countermeasures to mitigate cache-based attacks on virtualized network functions.

With the proliferation of new mobile devices, mobile cloud computing technology has emerged to provide rich computing and storage functions for mobile users. The explosive growth of mobile data has led to an increased demand for solutions that conserve storage resources. Data deduplication is a promising technique that eliminates data redundancy for storage. For mobile cloud storage services, enabling the deduplication of encrypted data is of vital importance to reduce costs and preserve data confidentiality. However, recently proposed solutions for encrypted deduplication lack the desired level of security and efficiency. In this paper, we propose a novel scheme for serverless efficient encrypted deduplication (SEED) in mobile cloud computing environments. Without the aid of additional servers, SEED ensures confidentiality, data integrity, and collusion resistance for outsourced data. The absence of dedicated servers increases the effectiveness of SEED for mobile cloud storage services, in which user mobility is essential. In addition, noninteractive file encryption with the support of lazy encryption greatly reduces latency in the file-upload process. The proposed indexing structure (D-tree) supports the deduplication algorithm and thus makes SEED much more efficient and scalable. Security and performance analyses prove the efficiency and effectiveness of SEED for mobile cloud storage services.

Cloud storage provides scalable and low cost resources featuring economies of scale based on multi-tenant architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. However, deduplication leads to problems with data confidentiality, thereby necessitating secure deduplication solutions. Server-aided encryption schemes have been proposed to achieve the strongest confidentiality but with the cost of managing a key server (KS). Previous schemes, however, are based on a centralized KS that uses only a single secret key assuming a single KS in the system. In cloud storage where multi-tenancy and scalability are crucial, such schemes degrade not only the effectiveness of deduplication but also the scalability with increasing users. In this paper, we extend server-aided encryption to a decentralized setting that consists of multiple KSs. The key idea of our proposed scheme is to construct an inter-KS deduplication algorithm, by which a cloud storage service provider can perform deduplication over ciphertexts from different KSs within a tenant or across tenants. This way, our scheme simultaneously offers flexibility of KS management and cross-tenant deduplication over encrypted data. The novelty of the approach is using a decentralized architecture that does not require any centralized entities for the coordination or pre-sharing of secrets among KSs. Therefore, it allows cloud storage services to offer high deduplication efficiency and scalability while preserving strong data confidentiality. We show the result of performance analysis on the proposed scheme by conducting extensive experiments. In addition, our security analysis demonstrate that the proposed scheme satisfies all desired security properties.

To achieve high storage saving, data deduplication techniques are widely used in many practical cloud storage services, which removes redundant data and keeps only a single copy of them. However, secure data deduplication over encrypted data is challenging since encryption may result in different ciphertexts even when the original messages are the same. Thus, message-locked encryption (MLE) is proposed to solve this issue and demonstrates that it is secure under the unpredictable message set. Since block-level deduplication can achieve more fine-grained storage saving, several block-level deduplication schemes that support updatability are also vividly proposed. However, the previous updatable block-level MLE schemes are vulnerable against brute-force attack when the message set is predictable. Since the size of a block is typically much less than an arbitrary size of a file, the predictability problem is a very important pragmatic concern which should be addressed in the block-level deduplication literature. In this paper, thus, we propose a novel secure block-level deduplication scheme that guarantees efficient data update and brute-force attack resilience even when messages are predictable with the rigorous security proof. Also, our performance evaluation shows that additional time and bandwidth usage can be minimized as the size of a block increases.

There are recommendation systems everywhere in our daily life. The collection of personal data of users by a recommender in the system may cause serious privacy issues. In this article, we propose the first privacy-preserving matrix factorization for recommendation using fully homomorphic encryption. Our protocol performs matrix factorization over encrypted users’ rating data and returns encrypted outputs so that the recommendation system learns nothing on rating values and resulting user/item profiles. Furthermore, the protocol provides a privacy-preserving method to optimize the tuning parameters that can be a business benefit for the recommendation service providers. To overcome the performance degradation caused by the use of fully homomorphic encryption, we introduce a novel data structure to perform computations over encrypted vectors, which are essential for matrix factorization, through secure two-party computation in part. Our experiments demonstrate the efficiency of our protocol.

Secure deduplication using convergent encryption eliminates duplicate data and stores only one copy to save storage costs while preserving the security of the outsourced data. However, convergent encryption produces a number of encryption keys, of which size is linear to the number of different data. Although a deduplication scheme has been proposed for efficient convergent key management recently, it has drawbacks in terms of scalability and key management security. In order to solve these problems, we propose a novel secure deduplication scheme with scalable and reliable key management based on paring-based cryptography. The proposed scheme does not require additional secure channels to distribute key components while still guaranteeing secure key management as opposed to the previous schemes.

In cloud services, deduplication technology is commonly used to reduce the space and bandwidth requirements services by eliminating redundant data and storing only single copy. Deduplication is most effective when multiple users outsource the same data to the cloud storage, but raises issues relating to security and ownership. Proof-of-ownership schemes allow any owner of the same data to prove to the cloud storage server that he owns the data in a robust way. However, if encrypted data is outsourced into the cloud storage and the ownership changes dynamically, deduplication would be hampered. Thus, we propose a secure deduplication scheme that supports dynamic ownership management based on randomized convergent encryption in this study.

In this paper, we examine the online authentication method based on Merkle (hash) tree focusing on its reliability. Coming from side channels in online authentication, the effectiveness runs into danger in the long run. With consideration of effectiveness, we present a Merkle tree based online authentication resilient against side channels by obfuscating authentication proofs. Security and efficiency are analyzed to demonstrate the practicality of the proposed approach.

Recommendation systems become popular in our daily life. It is well known that the more the release of users' personal data, the better the quality of recommendation. However, such services raise serious privacy concerns for users. In this paper, focusing on matrix factorization-based recommendation systems, we propose the first privacy-preserving matrix factorization using fully homomorphic encryption. On inputs of encrypted users' ratings, our protocol performs matrix factorization over the encrypted data and returns encrypted outputs so that the recommendation system knows nothing on rating values and resulting user/item profiles. It provides a way to obfuscate the number and list of items a user rated without harming the accuracy of recommendation, and additionally protects recommender's tuning parameters for business benefit and allows the recommender to optimize the parameters for quality of service. To overcome performance degradation caused by the use of fully homomorphic encryption, we introduce a novel data structure to perform computations over encrypted vectors, which are essential operations for matrix factorization, through secure 2-party computation in part. With the data structure, the proposed protocol requires dozens of times less computation cost over those of previous works. Our experiments on a personal computer with 3.4 GHz 6-cores 64 GB RAM show that the proposed protocol runs in 1.5 minutes per iteration. It is more efficient than Nikolaenko et al.'s work proposed in CCS 2013, in which it took about 170 minutes on two servers with 1.9 GHz 16-cores 128 GB RAM.

With prevalence of remote storage services, data privacy issues become more serious owing to loss of control to outsourced data. In the meanwhile, the service providers tend to minimize storage utility costs. To minimize the storage costs while preserving data privacy, secure deduplication techniques have been proposed, which are categorized into client-side or server-side approaches. Client-side approach achieves storage and bandwidth savings at the same time but allows external adversaries to know existence of duplicates in the remote storage. On the contrary, server-side one prevents the adversaries from getting acknowledged but sacrifices network bandwidth savings. In fog computing, however, which is a new computing paradigm extending the cloud computing by outsourcing a centralized workload of the cloud to geographically distributed fog devices located at the edge of the networks, the previous deduplication schemes cannot guarantee efficiency improvement and privacy preservation simultaneously. In this paper, we present a simple but nontrivial solution of these contradictory issues in fog storage. The proposed hybrid secure deduplication protocol combines client-and server-side deduplications by taking untrustworthy fog storage environments into account. The client-side deduplication is applied in inter-network (i.e., cloud-fog network) communications to prevent network congestion at the network core, while the server-side deduplication is adopted in intra-network (i.e., user-fog network) communications to prevent information leakage via side channels for maximal data privacy. Performance and security analyses demonstrate the comparable efficiency of the proposed scheme with security enhancement.

Data deduplication is a technique that removes redundancy of data on the storage. For cloud storage services, enabling deduplication over encrypted data is of vital importance to achieve both cost savings and keeping data confidentiality simultaneously. Recently proposed solutions are not sufficient because of lacking desired level of security and efficiency. In this paper, we propose SEED, a novel scheme for serverless and efficient encrypted deduplication. Without aid of any additional servers, SEED provides strong confidentiality to the outsourced data. In addition, its non-interactive file encryption with support of lazy encryption greatly reduces latency in file uploading process. Security analysis and performance evaluations show the superior efficiency and effectiveness of SEED for cloud storage services.

In cloud storage services, deduplication technology is commonly used to reduce the space and bandwidth requirements of services by eliminating redundant data and storing only a single copy of them. Deduplication is most effective when multiple users outsource the same data to the cloud storage, but it raises issues relating to security and ownership. Proof-of-ownership schemes allow any owner of the same data to prove to the cloud storage server that he owns the data in a robust way. However, many users are likely to encrypt their data before outsourcing them to the cloud storage to preserve privacy, but this hampers deduplication because of the randomization property of encryption. Recently, several deduplication schemes have been proposed to solve this problem by allowing each owner to share the same encryption key for the same data. However, most of the schemes suffer from security flaws, since they do not consider the dynamic changes in the ownership of outsourced data that occur frequently in a practical cloud storage service. In this paper, we propose a novel server-side deduplication scheme for encrypted data. It allows the cloud server to control access to outsourced data even when the ownership changes dynamically by exploiting randomized convergent encryption and secure ownership group key distribution. This prevents data leakage not only to revoked users even though they previously owned that data, but also to an honest-but-curious cloud storage server. In addition, the proposed scheme guarantees data integrity against any tag inconsistency attack. Thus, security is enhanced in the proposed scheme. The efficiency analysis results demonstrate that the proposed scheme is almost as efficient as the previous schemes, while the additional computational overhead is negligible.