The publish/subscribe model has gained prominence in the Internet of things (IoT) network, and both Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) support it. However, existing coverage-based fuzzers may miss some paths when fuzzing such publish/subscribe protocols, because they implicitly assume that there are only two parties in a protocol, which is not true now since there are three parties, i.e., the publisher, the subscriber and the broker. In this paper, we propose MultiFuzz, a new coverage-based multiparty-protocol fuzzer. First, it embeds multiple-connection information in a single input. Second, it uses a message mutation algorithm to stimulate protocol state transitions, without the need of protocol specifications. Third, it uses a new desockmulti module to feed the network messages into the program under test. desockmulti is similar to desock (Preeny), a tool widely used by the community, but it is specially designed for fuzzing and is 10x faster. We implement MultiFuzz based on AFL, and use it to fuzz two popular projects Eclipse Mosquitto and libCoAP. We reported discovered problems to the projects. In addition, we compare MultiFuzz with AFL and two state-of-the-art fuzzers, MOPT and AFLNET, and find it discovering more paths and crashes.

Self-sovereign identity (SSI) is a new distributed method for identity management, commonly used to address the problem that users are lack of control over their identities. However, the excessive pursuit of self-sovereignty in the most existing SSI schemes hinders sanctions against attackers. To deal with the malicious behavior, a few SSI schemes introduce accountability mechanisms, but they sacrifice users’ privacy. In addition, the digital identities (static strings or updatable chains) in the existing SSI schemes are as inputs to a third-party executable program (mobile app, smart contract, etc.) to achieve identity reading, storing and proving, and users’ self-sovereignty are weakened. To solve the above problems, we present a new self-sovereign identity scheme to strike a balance between privacy and accountability and get rid of the dependence on the third-party program. In our scheme, one and only individual-specific executable code is generated as a digital avatar-i for each human to interact with others in cyberspace without a third-party program, in which the embedding of biometrics enhances uniqueness and user control over their identity. In addition, a joint accountability mechanism, which is based on the shamir (t, n) threshold algorithm and a consortium blockchain, is designed to restrict the power of each regulatory authority and protect users’ privacy. Finally, we analyze the security, SSI properties and conduct detailed experiments in terms of the cost of computation, storage, and blockchain gas. The analysis results indicate that our scheme resists the known attacks and fulfills all the six SSI properties. Compared with the state-of-the-art schemes, the extensive experiment results show that the cost is larger in server storage, blockchain storage, and blockchain gas, but is still low enough for practical situations.

Pixel value ordering (PVO) is an efficient method for implementing reversible data hiding, which can achieve embedding based on overlapping pixel blocks when combined with the flexible patch moving (FPM) mode, especially the two-dimensional (2D) FPM mode. However, the existing 2D FPM mode, whose pairing way of prediction error is not conducive to generating more pixel blocks available for embedding, and whose movement rules are too inefficient to fully exploit the potential of the PVO, results in wasting many available blocks. Therefore, in this paper, a two-stage embedding mechanism is proposed for the 2D FPM mode, in which the combination of prediction errors is adjusted to improve the possibility of generating available blocks and the two-stage embedding doubles the number of pixel blocks available for embedding. Furthermore, an FPM mode selection is proposed, where four novel 2D FPM modes are designed to efficiently exploit the potential of the PVO according to the different directional gradients. Lastly, a set of efficient 2D mappings is well-designed for multiple histograms to achieve lower embedding distortion. The extensive experimental results show that the proposed method outperforms other state-of-the-art methods in terms of embedding capacity and image fidelity. The average peak signal-to-noise ratio for the Kodak image dataset is as high as 63.62 dB after embedding 10,000 bits.

In recent years, reversible data hiding in encrypted domain (RDH-ED) has garnered considerable interest among researchers, resulting in the development of high-performance methods based on various carriers. However, the challenge of enhancing the data embedding capacity while ensuring reversibility becomes increasingly pronounced when the carrier is a three-dimensional (3D) model. In this paper, a high capacity RDH-ED method based on dynamic prediction and virtual connection for 3D models is proposed. Unlike existing methods that partition the vertices in the model into embeddable and prediction sets, where each vertex can only serve one function, the proposed dynamic prediction mechanism constructs a data embedding order set by leveraging the connectivity relationships between vertices. This allows each vertex within the set to both embed data and provide predictions, significantly increasing the proportion of embeddable vertices. Moreover, the proposed method is the first work to consider independent vertices within the model and integrates a novel virtual connection approach with the dynamic prediction process, enabling all independent vertices to participate in data embedding and prediction, thereby further enhancing the data embedding capacity. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art methods in terms of data embedding capacity while ensuring reversibility.

In recent years, the combination of power systems and IoT (Internet of things) has become a new generation of smart grid. Cloud computing is used to provide storage and computing services for smart grid data due to its convenience. Generally, searchable encryption technology is considered a feasible solution to guarantee data security and support search function. Li proposed a searchable symmetric encryption scheme based on pseudo-random function for smart grid data in 2019. In this paper, we propose a more efficient and more secure searchable symmetric encryption scheme for smart grid data. The scheme improves the search efficiency by introducing the Bloom filter and changing the structure of the index. Specifically, we first narrow the search scope and then perform a second search, which eliminates the false positive caused by the introduction of the Bloom filter. At the same time, we assign an ID to each piece of data (a row of data) in a tabular dataset, store the hash value of the ID (adding salt) in the index, and the search results are returned by the server contain none of the ID information, which improves security. Experiments on real data show that our scheme is 52% more efficient than the previous scheme.

Aiming at the problems of low text utilization rate and ambiguity of secret information extraction in the “tag + keyword” coverless information hiding methods, we propose a coverless information hiding method of Chinese text based on polynomial encryption in this paper. The secret data communication is realized by the analogously forged URLs transmission, which improves the security of mobile computing to avoid the risk that the carrier texts are attacked maliciously. The text utilization rate and retrieval success rate are improved by using tags to select multiple keywords to expand the number of keywords in the index table. The keyword’s location in secret message is obtained through text vocabulary matching, and the tag index and location information are encrypted by polynomial for secret information transmission. The split keywords of secret message can be transmitted in disorder. The experimental result shows that the secret message can be successfully extracted by sorting the extracted keywords according to the frequency and relevance of their combined tags. In our experiments with different test databases, both the hiding success rate and the average hiding capacity are improved with higher test values than other existing methods.