Data deduplication has attracted many cloud service providers (CSPs) as a way to reduce storage costs. Even though the general deduplication approach has been increasingly accepted, it comes with many security and privacy problems due to the outsourced data delivery models of cloud storage. To deal with specific security and privacy issues, secure deduplication techniques have been proposed for cloud data, leading to a diverse range of solutions and trade-offs. Hence, in this article, we discuss ongoing research on secure deduplication for cloud data in consideration of the attack scenarios exploited most widely in cloud storage. On the basis of classification of deduplication system, we explore security risks and attack scenarios from both inside and outside adversaries. We then describe state-of-the-art secure deduplication techniques for each approach that deal with different security issues under specific or combined threat models, which include both cryptographic and protocol solutions. We discuss and compare each scheme in terms of security and efficiency specific to different security goals. Finally, we identify and discuss unresolved issues and further research challenges for secure deduplication in cloud storage.

Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to identify vulnerabilities automatically, and many publications have been released to date. However, fuzzing for embedded systems has not been studied extensively owing to various obstacles, such as multi-architecture support, crash detection difficulties, and limited resources. Thus, the article introduces fuzzing techniques for embedded systems and the fuzzing differences for desktop and embedded systems. Further, we collect state-of-the-art technologies, discuss their advantages and disadvantages, and classify embedded system fuzzing tools. Finally, future directions for fuzzing research of embedded systems are predicted and discussed.

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.

Cache side channel attacks extract secret information by monitoring the cache behavior of a victim. Normally, this attack targets an L3 cache, which is shared between a spy and a victim. Hence, a spy can obtain secret information without alerting the victim. To resist this attack, many detection techniques have been proposed. However, these approaches have limitations as they do not operate in real time. This article proposes a real-time detection method against cache side channel attacks. The proposed technique performs the detection of cache side channel attacks immediately after observing a variation of the CPU counters. For this, Intel PCM (Performance Counter Monitor) and machine learning algorithms are used to measure the value of the CPU counters. Throughout the experiment, several PCM counters recorded changes during the attack. From these observations, a detecting program was implemented by using these counters. The experimental results show that the proposed detection technique displays good performance for real-time detection in various environments.

With the successful proliferation of data outsourcing services, security and privacy issues have drawn significant attention. Data authentication in particular plays an essential role in the storage of outsourced digital content and keeping it safe from modifications by inside or outside adversaries. In this paper, we focus on online data authentication using a Merkle (hash) tree to guarantee data integrity. By conducting in-depth diagnostics of the side channels of the Merkle tree-based approach, we explore novel solutions to improve the security and reliability of the maintenance of outsourced data. Based on a thorough review of previous solutions, we present a new method of inserting auxiliary random sources into the integrity verification proof on the prover side. This prevents the exposure of partial information within the tree structure and consequently releases restrictions on the number of verification execution, while maintaining desirable security and reliability of authentication for the long run. Based on a rigorous proof, we show that the proposed scheme maintains consistent reliability without being affected by continuous information leakage caused by repetitions of the authentication process. In addition, experimental results comparing with the proposed scheme with other state-of-the-art studies demonstrate its efficiency and practicality.

Modern OSs expose an interface for monitoring CPU temperature to unprivileged users for effective user decision-based thermal management. Due to the low sampling rate and resolution, thermal sensors have generally been restricted to the construction of covert channels. However, exposing the thermal interface to unprivileged users may be problematic, because the heat emission inside a CPU core is affected by program execution on the core; an attacker may be able to infer the secret information of the program by exploiting the thermal interface as a side-channel. In this paper, we extensively analyze digital thermal sensors in Intel CPUs and show that it is possible to implement a software-based thermal side-channel attack. Specifically, by analyzing some properties of the thermal sensors, we inferred that the thermal sensor makes it possible to distinguish between a cache hit and a physical memory access in memory load operations. Based on the analysis results, we implement ThermalBleed, a thermal side-channel attack that breaks kernel address space layout randomization (KASLR) in Linux systems. Moreover, by conducting an in-depth analysis, we identify useful hidden properties of the Intel thermal sensors. Our analysis establishes a stepping stone to build a more precise and effective thermal side-channel attack in the future. To the best of our knowledge, this is the first work that extends a thermal covert channel to a practical side-channel attack by exploring the properties of Intel digital thermal sensors.