Computer vision and deep neural networks have been significantly promoting the development of visual perception in these years. Particularly, for autonomous vehicles, real-time image/video data is captured by onboard cameras and analyzed by computer vision techniques in many real applications. In the captured camera data, some contents can be used as auxiliary information to infer individuals' locations and trajectories, which leads to severe privacy leakage but has been rarely studied. Thus, the goal of this article is to protect individuals' location privacy by hiding side-channel information in the captured data while preserving the data utility for downstream applications. To this end, the technology of generative adversarial networks (GAN) is utilized to design two novel models, named ADGAN-I and ADGAN-II, both of which can take the original camera data as inputs and generate privacy-preserving outputs according to predefined sensitive object class. Thus, the processed camera data can defend location inference attack from adversaries in offline applications. Moreover, in ADGAN-I and ADGAN-II, the tradeoff between location privacy and data utility can be effectively balanced. Finally, the results of extensive real-data experiments validate the superiority of our proposed models over the state of the arts in utility preservation and privacy protection for autonomous vehicles' images and videos.

Due to the abundant marine resources, smart ocean has attracted much attention of the government, industry, and academy. The Internet-of-Things (IoT) architectures for smart ocean have been proposed to collect various of data from the ocean, thereby assisting environmental protection, military reconnaissance, and so on. However, few researchers have paid attention to the security and privacy issues of data collection and transmission. In this article, for the unreliable underwater environment, we present a secure, efficient, and complete data collection, and transmission and storage scheme for IoT in smart ocean. Especially, to prolong the lifetime of the underwater node, two novel data compression algorithms [lossy data compression algorithm (LCA) and lossless data compression algorithm (NLCA)] are also proposed. Moreover, due to the vulnerability of underwater nodes, we also propose a corresponding IoT framework and data collection pattern to resist the single point failure attack. Besides, to guarantee the confidentiality, reliability, and integrity of transmitting data, Elliptic Curve-ElGamal (EC-ElGamal) and elliptic curve digital signature algorithm (ECDSA) are employed. The consensus algorithm and blacklisting mechanism are also employed to detect and address failure or malicious nodes. Finally, the security analysis demonstrates that our scheme is able to resist many typical attacks for underwater nodes, such as manipulation attacks, Distributed Denial-of-Service (DDoS) attacks, malicious node injection attacks, and so on. Additionally, relevant experimental results show that the scheme is feasibility and efficiency.

Internet-of-Things (IoT) devices are getting more and more popular in recent years and IoT networks play an important role in the industry as well as people's activities. On the one hand, they bring convenience to every aspect of our daily life; on the other hand, they are vulnerable to various attacks that in turn cancels out their benefits to a certain degree. In this article, we target the defense techniques against IoT Distributed Denial-of-Service (DDoS) attacks and propose an edge-centric IoT defense scheme termed FlowGuard for the detection, identification, classification, and mitigation of IoT DDoS attacks. We present a new DDoS attack detection algorithm based on traffic variations and design two machine learning models for DDoS identification and classification. To demonstrate the effectiveness of the two machine learning models, we generate a large data set by DDoS simulators BoNeSi and SlowHTTPTest, and combine it with the CICDDoS2019 data set, to test the identification and classification accuracy as well as the model efficiency. Our results indicate that the identification accuracy of the proposed long short-term memory is as high as 98.9%, which significantly outperforms the other four well-known learning models mentioned in the most related work. The classification accuracy of the proposed convolutional neural network is up to 99.9%. Besides, our models satisfactorily meet the delay requirements of IoT when deployed in edge servers with computational powers higher than a personal computer.

Edge servers (ESs) are utilized to achieve the storage and sharing of IoT data. However, even if ES brings us much benefit, it also leads to many serious privacy leakage issues because users' data in ESs are out of control. Moreover, ES providers may also disclose user's private-sensitive data. Hence, in this article, we present a privacy-preserving, recoverable, and revocable edge data sharing scheme. In this scheme, we propose a novel attribute revocation chain based on the blockchain technology to achieve attribute revocation in ciphertext-policy attribute-based encryption (CP-ABE). Meanwhile, a secret sharing scheme (SSS) is introduced to assist the data recovery. Especially, for the situation that a single ES is hijacked, we also propose a corresponding efficient detection mechanism and key updating policy to promise the subsequent security of the whole system. Moreover, this scheme also resists Economic Denial-of-Sustainability (EDoS) attacks which are launched by some malicious users. The analysis shows that the proposed scheme can protect user's privacy and resist many attacks. Additionally, relevant experimental results demonstrate that our scheme has low computational overhead on the user side.

Smart home systems have become more and more prevailent in recent years. On the one hand, they make our everyday life more convenient; on the other hand, they suffer from the two notorious security problems, namely, the open-port problem and the overprivilege problem, making their security situations extremely worrying and uncheerful. In this article, we proposed HomeShield, a novel credential-less authentication framework to shield smart home systems by effectively defending against the attacks resulted from these two security problems without the need for sensitive credentials. We further detailed an implementation of HomeShield based on the side channels that are publicly available in Android smartphones serving as controllers of smart home systems and presented its workflow in protecting against various attacks caused by the open-port and overprivilege problems. Finally, we tested our HomeShield implementation on a real-world smart home system and considered four threat models that cover basically all practical attacks, including Mirai and its variants. We also considered the effectiveness of our HomeShield implementation on the SmartApps of the Samsung SmartThings platform, which also suffers from the open-port and overprivilege problems, even though its overprivilege issue has been extensively studied by the recently proposed works, such as ContexIoT and SmartAuth. The evaluation results indicate that our HomeShield realization can successfully defend against over 90% attack trials with an average latency of less than 1 s.

The concept of smart grid gained tremendous attention among researchers and utility providers in recent years. How to establish a secure communication among smart meters, utility companies, and the service providers is a challenging issue. In this paper, we present a communication architecture for smart grids and propose a scheme to guarantee the security and privacy of data communications among smart meters, utility companies, and data repositories by employing decentralized attribute based encryption. The architecture is highly scalable, which employs an access control Linear Secret Sharing Scheme (LSSS) matrix to achieve a role‐based access control. The security analysis demonstrated that the scheme ensures security and privacy. The performance analysis shows that the scheme is efficient in terms of computational cost.

Matrix inverse computation is one of the most fundamental mathematical problems in large-scale data analytics and computing. It is often too expensive to be solved in resource-constrained devices such as sensors. Outsourcing the computation task to a cloud server or a fog server is a potential approach as the server is able to perform large-scale scientific computations on behalf of resource-constrained users with special software. However, outsourcing brings in new security concerns and challenges such as data privacy violations and result invalidation. In this paper, we propose a secure and verifiable outsourcing scheme to compute the matrix inverse in a server. In our scheme, the client generates two secret key sets based on two chaotic systems, which are utilized to create two sparse matrices whose permuted versions are used for matrix encryption and decryption to protect input and output privacy. The server computes the inverse over the ciphertext matrix and returns the result to the client who can verify the validity of the inverse. We analyze the proposed scheme in terms of correctness, security, verifiability, and attack resistance, and compare its performance (computation, storage, and communication overheads) with those of the state-of-the-art. Our theoretical results and comparison study demonstrate that the proposed scheme provides a secure and efficient outsourcing mechanism for matrix inverse computation.