As cloud storage becomes popular, more and more users tend to outsource their data to powerful cloud severs. To prevent a single point of failure, users prefer to store data on multiple cloud servers from different cloud service providers. However, after outsourcing data to cloud servers, users lose the control of their data, which may incur many serious security issues, such as abnormal data tampering and deleting. It is necessary for users to audit multiple cloud storage aperiodically. In this article, we aim to design a decentralized self-auditing solution for multiple cloud storage, in which cloud servers can audit the integrity of each other, and thus no third-party entity is required. First, based on basic algebra, our protocol realizes decentralized self-auditing multiple cloud storage that only involves encrypted user data. Second, we design a proof exchanging mechanism, making any number of cloud servers form the same final integrity proof with a linear number of interactions. Third, our solution can achieve cloud dynamics, which can freely enable and disable a cloud server to provide storage service. At last, security proof and performance evaluation show that the proposed protocol has provable security and high efficiency.

To verify the integrity of data stored in the cloud and improve storage efficiency, numerous cloud storage auditing schemes with deduplication have been proposed. In cloud storage, when users perform data dynamic operations, they should lose ownership of original data. However, existing schemes require re-encrypting the entire ciphertext when ownership changes and recalculating the authenticators for the blocks following the updated blocks when insertion or deletion operations are performed. These processes lead to high computation overhead. To address the above issues, we construct a secure deduplication and cloud storage auditing scheme with efficient dynamic ownership management and data dynamics. We adopt CAONT encryption method, where only a portion of the updated block is required to be re-encrypted during the ownership management phase, significantly reducing computation overhead. We also implement index switch sets to maintain the mapping between block indexes and cloud storage indexes of ciphertext blocks. By embedding cloud storage indexes within the authenticators, our scheme avoids the need to recalculate authenticators when users perform dynamic operations. Additionally, our scheme supports block-level deduplication, further improving efficiency. Through comprehensive security analysis and experiments, we validate the security and effectiveness of the proposed scheme.

Remote data integrity auditing ensures the integrity of cloud storage. In practice, cloud users may not want their sensitive data to be exposed to others. Thus, it is meaningful to investigate how to realize data sharing with sensitive information hiding in cloud storage auditing. Up to now, cloud storage has been proven to achieve the sensitive information hiding property through a third-party sanitizer dedicated to sanitize user data, which leads to high outlays on purchasing and maintaining a special server. To meet this challenge, we design a novel cloud storage auditing protocol to support sensitive information hiding without the need of a third-party sanitizer. In addition, our scheme allows data owners to enable or disable other users to access their sensitive information with the help of the cloud that dose not deviate from the agreement during access control. To be specific, only after receiving the delegations from the data owner, the users can compute the valid warrants that can pass the access verification of the cloud. The proposed protocol is built on identity-based cryptography, thus avoiding the complex certificate management. We validate the advantages of the proposed protocol through massive theoretical analysis and experimental results.

Cloud storage is more and more prevalent in practice, and thus how to check its integrity becomes increasingly essential. A classical solution is identity-based (ID-based) provable data possession (PDP), which supports certificateless cloud storage auditing without entire user data. However, existing ID-PDP protocols always require that cloud users outsource data blocks, authenticators and a small-sized file tag to the cloud, and make use of the heavy elliptic curve cryptography over bilinear pairing. These disadvantages would result in vast storage, communication, and computation costs, which is unexpected, especially for resource-limited cloud users. To improve the performance, this paper proposes a novel cryptographic primitive: ID-based PDP with compressed cloud storage. In this model, cloud storage auditing can be achieved by using only encrypted data blocks in a self-verified way, and original data blocks can be reconstructed from the outsourced data. Thus, data owners no longer need to store original data blocks on the cloud. We also use some basic algebraic operations to realize a concrete ID-based PDP protocol with compressed cloud storage, which is quite efficient due to no heavy cryptographic operations involved. The proposed protocol can easily be extended to support the other practical functions by using the primitive replacement technique. The proposed protocol is strictly proven to have the properties of correctness, privacy, unforgeability and detectability. Finally, we give plenty of theoretical analysis and experimental results to validate the efficiency of the proposed protocol.

With the widespread application of cloud storage, ensuring the integrity of user outsourced data catches more and more attention. To remotely check the integrity of cloud storage, plenty of protocols have been proposed, implemented by checking the equation constructed by the aggregated blocks, tags, and indices. However, the verifier only has the knowledge of the indices of the audited blocks and tags, which thus requires the cloud to store both data blocks and tags for integrity verification. In this article, we present a compressive secure cloud storage protocol inspired by Goldreich-Goldwasser-Halevi (GGH) cryptosystem. Since the aggregated blocks can be reconstructed from the aggregated tags without the help of data indices, the cloud can only store data tags for providing the verifiable integrity proof. In this way, communication and storage costs can be hugely reduced and user private information can be hidden from the cloud. Furthermore, the proposed protocol only contains a few basic algebraic operations, making it highly efficient. We also provide formal security proof of the proposed protocol regarding forge, replay and replace attacks. In addition, we explore a new technique to support data dynamics. Furthermore, we establish a generic framework of compressive secure cloud storage protocols. Finally, we provide the theoretical analysis and experimental results, which further validate the effectiveness of the proposed protocol.

The potential of multiuser MIMO (MU-MIMO) to increase network capacity has been intensively studied. To enable concurrent transmission in Frequency-Division Duplex (FDD) systems with limited feedback, users have to report the estimated channel state information (CSI) to the base station (BS) for interference elimination, which however has been proven to be vulnerable. In this paper, we reveal how to utilize the CSI feedbacks to launch sniffing attacks in a deterministic way, where thesniffing attackenables the attackers to eavesdrop other users in the same transmission group. We conduct a rigorous theoretical analysis on the construction of the forged CSI. In the proposed sniffing attacks, the malicious users can successfully sniff other users’ messages without knowing their own messages for signal cancellation. To make sniffing attacks more practical, we explore the possibilities of sniffing multiple users by a coalition of malicious users or a single malicious user. To thwart sniffing attacks, we design a novel secure CSI feedback (SCF) protocol based on the random masking technique, which requires simple modifications at the BS and incurs a little additional workload. Extensive theoretical analysis and experimental results are provided to further validate the effectiveness of our proposed sniffing attacks and the corresponding countermeasure.

Orthogonal frequency division multiplexing (OFDM) signals are widely employed in most wireless comm- unication systems, and the problem of spectrum sensing for OFDM signals has been extensively investigated. However, most of the available research reports on spectrum sensing for OFDM signals ignore the existence of pilots, which are usually inserted in the OFDM signals for synchronization, channel estimation and control information delivery. To sense weak OFDM signals with periodic pilots reliably in cognitive radio networks, we propose two likelihood ratio test (LRT) detectors for the scenarios with and without time synchronization information. In the ideal scenario that an unlicensed secondary user is perfectly synchronized with a licensed primary user, we propose a near-optimal LRT (NOLRT) detector, which constructs the test statistic by correlating the locally generated pilots with the received OFDM signal samples. Since the NOLRT detector relies on perfect time synchronization that is difficult to realize in practice, we further propose a more practical buffer-aided LRT (BALRT) detector that does not require time synchronization. To formulate the test statistic, the BALRT detector stores the averages of historical OFDM signal samples in a buffer and then correlates them with the currently received OFDM signal samples. Theoretical closed-form expressions for the probability of detection and probability of false alarm are derived for the proposed detectors. To alleviate the negative effects of noise uncertainty in low SNR scenarios, we also propose an effective method based on noise power estimation. Monte Carlo simulation results match with theoretical results very well and show that the NOLRT detector outperforms the counterparts significantly in additive white Gaussian noise (AWGN) channel with perfect time synchronization, while the BALRT detector outperforms the counterparts noticeably in multipath propagation environments without time synchronization.

Summary The overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN‐based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.

With the development of cloud storage, data owners no longer physically possess their data and thus how to ensure the integrity of their outsourced data becomes a challenging task. Several protocols have been proposed to audit cloud storage, all of which rely mainly on data block tags to check data integrity. However, their block tag constructions employ cryptographic operations, which makes them computationally complex. In this paper, we investigate a secure cloud storage protocol based on the classic discrete logarithm problem. Our protocol generates data block tags with only basic algebraic operations, which brings substantial computation savings compared with previous work. We also strictly prove that the proposed protocol is secure under a definition which captures the real-world uses of cloud storage. In order to fit more application scenarios, we extend the proposed protocol to support data dynamics by employing an index vector and third-party public auditing by using a random masking number, both of which are efficient and provably secure. At last, theoretical analysis and experimental evaluation are provided to validate the superiority of the proposed protocol.

In this paper, we present experimental studies on Multi-channel Multi-radio Multi-hop (M3) wireless networks. The interference of multiple channels and multiple radios is studied. The result may help to understand effect of multi-channel and multi-radio in real system and is useful as reference for network performance optimization. We evaluate the coexistence of real-time traffic and best-effort traffic in multi-hop wireless networks. Our experiments show that coordination among wireless nodes is critical for optimal network performance, especially in the scenarios with quality of service (QoS) requirements. To perform the experiments, an M3 testbed with 32 nodes is built. The testbed is based on Windows operating system and provides convenient programming interface and management tools. The architecture and key modules of the testbed design are described.