In the Internet of Medical Things (IoMT), ensuring the confidentiality, integrity, and authenticity of sensitive medical data poses a significant challenge. Signcryption, merging digital signature and public-key encryption into one logical step, offers a promising solution for such resource-constrained networks. Most existing signcryption schemes inherently lack public verification, which prevents third parties (such as routers) from early identifying illegal messages without decryption. In this paper, we propose a practical certificateless aggregate signcryption scheme with public verification for IoMT. Our design uses elliptic curves and follows the certificateless cryptosystem framework. Thus, it not only avoids time-consuming pairing operations but also addresses the issues of certificate management and key escrow. Meanwhile, individual signcryptions can be compressed into a single aggregate signcryption, optimizing computational and communication overheads. Furthermore, both the pre-aggregated and aggregated signcryptions support public verification, effectively enabling the early elimination of illegal messages. We further present formal security proofs for our construction and reduce its security to the hardness of the one-sided gap Diffie-Hellman (OGDH) and the elliptic curve discrete logarithm (ECDL) problems. Performance analysis reveals that, compared with prior studies, this scheme preemptively blocks illegal message spread, reduces receiving-end load, and better suits resource-constrained IoMT.

Feature unlearning, forgetting sensitive features while maintaining the accuracy of models, is a pressing issue against Feature Inference Attacks (FIA) in Vertical Federated Learning (VFL). This issue is addressed by retraining the model from scratch on a dataset without the sensitive features from scratch or Federated Unlearning (FU) for all samples. However, they either introduce high overheads due to retraining or reduce the accuracy of the unlearned model. In this paper, we proposedCer-FeaUn, a certified feature unlearning, trading off between the overheads and accuracy. Specifically, a novelfeature perturbation strategyis first proposed to construct a perturbed dataset, where the sensitive features are perturbed with noises. Then, the effect is defined as the parameters difference between models trained with the original and perturbed dataset. Finally, an unlearned model is trained in first-order, where the effect is removed from the original model in one epoch. Furthermore, Cer-FeaUn performscertified removalfor server-side models with strongly convex loss functions. That is, the distribution of the unlearned model is statistically indistinguishable from that of the retrained model. For the scenario with a few sensitive features, simulation results show that the accuracy of the unlearned model is up to 84.79%, and the runtime of Cer-FeaUn is 15 times faster than that of the retrained model.

Side-channel attack, which exploits time information leakage during the digital signature generation process, poses a severe threat to the confidentiality and integrity of transactions in blockchain payment channels. Currently, Transformer-based detection methods are effective at capturing long-term dependencies, while Long Short-Term Memory (LSTM) networks excel at modeling short-term dynamic time-series features. However, existing approaches struggle to uniformly model both long-term and short-term time-series features, limiting their performance in anomaly detection for complex transaction sequences. In this article, we propose TLD-SCA, a novel side-channel attack detection model that innovatively integrates Transformer’s capability for global dependency modeling with LSTM’s advantage in capturing local time-series dynamics. This enables long-term and short-term time-series analysis of transaction timing data. Experimental results demonstrate that TLD-SCA significantly outperforms existing methods in terms of accuracy (99.5%), precision (99.2%), and recall (98.3%), thereby providing a higher level of security assurance for blockchain payment channels.

Session-based multimedia recommendation in edge computing remains an important issue for boosting the utilization of services since service composition has increasingly attracted attention. Existing session-based recommendations (SBRs) model the session sequence with multilevel feature extraction in graph neural networks (GNNs). However, multilevel feature extraction in disentangled graph neural networks causes over-smoothing and privacy leakage. To address the aforementioned problems, Secure and Efficient Session-based Multimedia Recommendation (SeSMR) model is proposed. In the proposed SeSMR model, based on BGV homomorphic encryption, a ciphertext training submodel is proposed to address the privacy leakage, ensuring the security in SBR. Furthermore, based on the reinforcement of feature activation, a residual attention mechanism is proposed to mitigate over-smoothing while maintaining the independence of multiple features. Finally, based on location coding, a soft attention mechanism is proposed to improve the recommendation accuracy, by introducing the position difference information between items into intra-session and inter-session scenarios. Experiments demonstrate that both Recall and MRR metrics exhibit nearly 2% to 5% improvement.

Cloud storage technology is constantly evolving, resulting in a significant amount of duplicate data being stored in the cloud, particularly multimedia data such as images and videos. In terms of data privacy and storage optimization, the encrypted deduplication should be checked to save space overhead for cloud servers. Compared to exact deduplication, fuzzy deduplication is low-cost for encrypted multimedia data. Focusing on reducing the false deletion rate, an efficient and secure fuzzy deduplication system based on dual-feature without additional servers is proposed. We also propose a concept of pre-verification for label consistency to compensate for the loss that cannot be fixed through post-verification. Therefore, it is more practical. Finally, we conduct experiments on real-world datasets for performance evaluation. The experimental results show good performance in terms of both computational cost and deduplication efficiency.

In the era of the Internet of Things (IoT), remote sensors and endpoint appliances generate vast amounts of data. Decentralized and collaborative learning builds on these IoT data to enable classification and recognition tasks by inviting multiple data owners. Federated learning (FL), as a popular collaborative learning framework, can significantly improve the performance of models without collecting the original data. To invite data owners to participate in FL, various incentive mechanisms are designed to address this issue by researchers. However, existing solutions still face high costs and low utility due to information asymmetry, where the reputation, computation power, and data quantity of the data owners are not known in advance. Therefore, we propose a Stackelberg game-based multifactor incentive mechanism for FL (SGMFIFL). First, we design the Top-$K$cost selection algorithm based on reverse auction, which can reduce the cost of selecting data owners. Next, we devise a multifactor reward function based on reputation, accuracy, and reward rate, the data owners with high reputation and high accuracy will be of more reward. In particular, to ensure that SGMFIFL can provide reliable incentives in IoT, we use blockchain to provide a secure and trusted environment. Finally, we construct a two-stage Stackelberg game model for the task publisher and the data owners and derive an optimal Equilibrium solution for both stages of the whole game. Experiments conducted on two well-known data sets, MNIST and CIFAR10, demonstrate the significant performance of the proposed mechanism.

Source location privacy (SLP) protection is an emerging research topic in wireless sensor networks. Because the source location represents the valuable information of the target being monitored and tracked, it is of great practical significance to achieve a high degree of privacy of the source location. Although many studies based on phantom nodes have alleviates the protection of SLP to some extent. It is urgent to solve the problems, such as complicate the ac path between nodes, improve the centralized distribution of phantom nodes near the source nodes and reduce the network communication overhead. In this paper, protection scheme based on sector phantom routing (PSSPR) routing is proposed as a visible approach to address SLP issues. We use the coordinates of the center node V to divide sector domain, which act an important role in generating a new phantom node. The phantom nodes perform specified routing policies to ensure that they can choose various locations. In addition, the directed random route can ensure that data packets avoid the visible range when they move to the sink node hop by hop. Thus, the source location is protected. Theoretical analysis and simulation experiments show that this protocol achieves higher security of source node location with less communication overhead.

With the development of network technology, privacy protection and users anonymity become a new research hotspot. The existing blockchain privacy-aware public key infrastructure (PKI) model can ensure the privacy of users in the authentication process to a certain extent, but there are still problems of the storage and leakage of users' keys. This paper first proposes a strong forward-secure ring signature scheme based on RSA, which ensures the anonymity of the signing users and the forward-backward security of the keys. Then, by introducing the ring signature technology into the privacy-aware PKI model, this paper proposes a privacy-aware PKI model with strong forward security based on block chains, which not only ensures the users' identity privacy, but also solves the problem of the storage and leakage of the users' keys, greatly improving the success rate and security of the users' identity authentication. Finally, this paper applies the proposed PKI model to anonymous transactions, designs a privacy-aware anonymous transaction model with strong forward security, realizing anonymous transactions without relying on trusted third parties, and implementing users' privacy protection.

Selfish mining attacks get a high prize due to the additional rewards unproportionate to their mining power (mining pools have particular advantages). Generally, this category of attacks stresses decreasing the threshold to maximize the rewards toward the view of attackers. Semi-selfish mining falls into the family of selfish mining attacks, where the threshold value is approximately 15%. However, it gets little attention to implement these attacks in practical. In this paper, we focus on the validity of semi-selfish mining attacks considering the probability of being detected. More specifically, we discuss mining strategies through backward deduction. That is to say that the attacking states derived from the observable states, which with normal forking rate, just as without semi-selfish mining attacks, toward the view of the honest miners. Rewards distribution is further investigated concerning these strategies. The simulation results indicate that it does not necessarily bring rewards advantage over large pools. Instead, the small pools have an advantage over the additional rewards. However, the probability for small pools to successfully implement these strategies is pretty low. That is, it is impossible for the pools, although profitable for them, to sponsor semi-selfish mining attacks without being detected.

Transaction data on the public chain is open and transparent to all participants, which poses a potential threat to the privacy of participants. Some privacy-conscious token holders want to employ obfuscation methods to protect the origin and destination of their tokens, and the need for token shuffling services (TSS) arose at a historical moment. The prevailing token shuffling policies rely too heavily on blacklists in the token shuffling process, which is contrary to the idea of decentralization. Therefore, weakening or even eliminating the usage of blacklisting mechanisms in TSSs, which is an urgent issue to be addressed. In this paper, we adopt the idea of machine learning and propose a general framework for TSSs, which replaces the natural role under incomplete information with machine learning, so as to achieve the goal of eliminating the blacklist mechanism in TSSs. Then, the token shuffling process is constructed as an extended game under incomplete information on the basis of different token shuffling policies, and this incomplete information game is analyzed on the basis of poison policy, haircut policy, and suicide policy respectively. Finally, the sequential equilibrium under different games is investigated through simulations. The simulation results show that in the incomplete information game based on the poison policy, the participation of two players in the TSS is a sequential equilibrium, while in the incomplete information game based on the haircut policy, the players do not participate in the TSS is an sequential equilibrium.

Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi-selfish miners), who mine on the private chain, to mine on public chain with a small probability ρ. Simulation results show that SMHMDP can trade off between revenues and forking rate. Put differently, selfish miners benefit from attacking within an acceptable forking rate toward the view of honest parties, without leading selfish mining attacks to be an armchair strategist.

vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad coins to be registered in the blacklist in time, users can only obtain part of the blacklist information, which allows illegal criminals to take advantage of it. How to prevent illegal activities under the partial information blacklist and how to design coins' quality updating rule rationally have become open issues in mixing coins. The updating rule of coins' quality in mixing is addressed since illegal criminals may carry out illegal activities, for example, money laundering. ImpSuic, an improved suicide strategy, is proposed as a new quality updating rule. The intuition is: all coins of the one who has the highest bad coins according to the blacklist, are recorded as bad coins. On the other hand, the coins' quality of others remain unchanged. Besides, linear programming is introduced into ImpSuic strategy to predict the maximum utility after mixing coins, which facilitates users to make reasonable decisions before mixing coins. Simulation results show that the quality updating rule in ImpSuic strategy can preserve users' privacy and antimoney launder.

Multi-key Fully homomorphic encryption (MFHE) schemes allow computation on the encrypted data under different keys. However, traditional multi-key FHE schemes based on Learning with errors (LWE) have the undesirable property that is the number of keys has to be fixed in advance. A dynamic multi-key FHE scheme is the most versatile variant which the information about the participants is not required before key generation. To support further homomorphic computation on extended ciphertexts and ciphertexts encrypted under additional keys, Peikert and Shiehian (TCC ’16) proposed a leveled dynamic multi-key FHE scheme. Nevertheless, it introduces the circular-security assumption for the LWE parameters to ensure its security, which provides weaker security to the scheme. The problem of how to construct a LWE-based dynamic multi-key FHE scheme is still open. To address the above problem, in this work, we present a dynamic multi-key FHE scheme based on the LWE assumption in public key setting. The ciphertext can be extended and performed homomorphic evaluation with the ciphertexts encrypted under additional keys. Compared with current constructions, our proposed method requires fewer “local” memory and the process of ciphertext extension is distributed. Our proposed method provides a new way to extend the ciphertext such that the ciphertext homomorphism computation is more efficient. Our scheme is proven to be secure under standard LWE assumptions without using the circular-security assumption.

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.

Adversaries in two-party computation may sabotage a protocol, leading to possible collapse of the information security management. In practice, attackers often breach security protocols with specific incentives. For example, attackers manage to reap additional rewards by sabotaging computing tasks between two clouds. Unfortunately, most of the existing research works neglect this aspect when discussing the security of protocols. Furthermore, the construction of corrupting two parties is also missing in two-party computation. In this paper, we propose an incentive-driven attacking model where the attacker leverages corruption costs, benefits and possible consequences. We here formalize the utilities used for two-party protocols and the attacker(s), taking into account both corruption costs and attack benefits. Our proposed model can be considered as the extension of the seminal work presented by Groce and Katz (Annual international conference on the theory and applications of cryptographic techniques, Springer, Berlin, pp 81–98, 2012 ), while making significant contribution in addressing the corruption of two parties in two-party protocols. To the best of our knowledge, this is the first time to model the corruption of both parties in two-party protocols.