With the increasing rates of interconnected Internet of Things (IoT) devices within Software-Defined Networking (SDN) environments, distributed denial of service (DDoS) attacks have become increasingly common. As a result of this challenge, novel detection and classification methods must be developed based on the unique characteristics of SDN-supported IoT networks. This paper proposes a novel approach to detecting and categorizing DDoS attacks that has been optimized specifically for such environments. As part of our methodology, we integrate convolutional neural networks (CNN) and long-short-term memory (LSTM) models into a multilevel deep neural network architecture. With this hybrid architecture, complex spatial and temporal patterns can be automatically extracted from raw network traffic data to facilitate comprehensive analysis and accurate identification of DDoS attacks. We validate the efficacy and superiority of our proposed approach over traditional machine learning algorithms by conducting rigorous experiments on real-world datasets. Our findings underscore the potential of the multi-level deep neural network approach as a robust and scalable solution for mitigating DDoS attacks in SDN-supported IoT networks. By improving network security and resilience to evolving threats, our methodology contributes to safeguarding critical infrastructures in the era of interconnected IoT ecosystems.

Recommendation systems powered by artificial intelligence (AI) are widely used to improve user experience. However, AI inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges can protect users’ personal information, benefit service providers, and foster service ecosystems. Presently, numerous techniques based on differential privacy have been proposed to solve this problem. However, existing solutions encounter issues such as inadequate data utilization and a tenuous trade-off between privacy protection and recommendation effectiveness. To enhance recommendation accuracy and protect users’ private data, we propose ID-SR, a novel privacy-preserving social recommendation scheme for trustworthy AI based on the infinite divisibility of Laplace distribution. We first introduce a novel recommendation method adopted in ID-SR, which is established based on matrix factorization with a newly designed social regularization term for improving recommendation effectiveness. We then propose a differential privacy-preserving scheme tailored to the above method that leverages the Laplace distribution’s characteristics to safeguard user data. Theoretical analysis and experimentation evaluation on two publicly available datasets demonstrate that our scheme achieves a superior balance between privacy protection and recommendation effectiveness, ultimately delivering an enhanced user experience.

Abstract We present a new zero-knowledge succinct argument of knowledge (zkSNARK) scheme for Rank-1 Constraint Satisfaction (RICS), a widely deployed NP-complete language that generalizes arithmetic circuit satisfiability. By instantiating with different commitment schemes, we obtain several zkSNARKs where the verifier’s costs and the proof size range fromO(log2N) to O(N) O\left( {\sqrt N } \right) depending on the underlying polynomial commitment schemes when applied to anN-gate arithmetic circuit. All these schemes do not require a trusted setup. It is plausibly post-quantum secure when instantiated with a secure collision-resistant hash function. We report on experiments for evaluating the performance of our proposed system. For instance, for verifying a SHA-256 preimage (less than 23k AND gates) in zero-knowledge with 128 bits security, the proof size is less than 150kB and the verification time is less than 11ms, both competitive to existing systems.

Wireless body area networks (WBAN) enable ubiquitous monitoring of patients, which can change the future of healthcare services overwhelmingly. As the collected data of patients usually contain sensitive information, how to collect, transfer, store and share data securely and properly has become a concerning issue. Attribute-based encryption (ABE) can achieve data confidentiality and fine-grained access control simultaneously. Identity-based ring signature (IBRS) allows patients to prove their identity without leaking any extra (private) information. However, the heavy computational burden of ABE and IBRS is intolerable for most power-limited mobile devices, which account for a large proportion of WBAN devices. This paper combines the attribute-based online/offline encryption (ABOOE) and IBRS to achieve an outsourced online/offline hybrid signcryption ($O^{3}$HSC) scheme. As far as we know, this scheme is the first signcryption scheme that adopts IBRS and satisfies online/offline signcryption simultaneously.$O^{3}$HSC divides the key generation and signcryption into offline and online phases to increase the throughput of the central authority and save the power resources of mobile devices, respectively. Besides, outsourced decryption and public signature verification are also realized.$O^{3}\mathrm {HSC}$achieves security under CCA and CMIA, and the performance analysis shows that$O^{3}\mathrm {HSC}$is a lightweight and applicable scheme for WBAN.

Linear map commitments allow the prover to commit to a vector, with the ability to prove the image of a linear map acting on the vector. In this paper, we propose linear map commitments with updatable feature and perfectly hiding property. Updatable feature means that the prover can update the commitment more efficiently than recompute the commitment when some of the entries in the committed vector are changed. Perfectly hiding property ensures the commitment reveals no information about the committed vector before opening. Then we present the implementation of our updatable linear map commitment (ULMC) over the 256-bit BN curve recommended in the SM9 standard, which provides around 100-bit security. The implementation shows that our ULMC schemes are efficient enough to support the elementary database constructions that simultaneously permit batching membership test, linear combination test, updatable feature and authenticity. Finally, we show that the ULMC-powered elementary databases are capable of supporting various applications where privacy and trust are the first priority such as exam result management systems, Internet of Things (IoT) management systems and business operations between banks and enterprises.

In 2007, Carlet and Ding introduced two parameters, denoted by NbF and NBF, quantifying respectively the balancedness of general functions F between finite Abelian groups and the (global) balancedness of their derivatives DaF(x) = F(x + a) - F(x), a ∈ G \ {0} (providing an indicator of the nonlinearity of the functions). These authors studied the properties and cryptographic significance of these two measures. They provided inequalities relating the nonlinearity NL(F) to NBF for S-box and specifically obtained an upper bound on the nonlinearity that unifies Sidelnikov-Chabaud-Vaudenay's bound and the covering radius bound. At the Workshop WCC 2009 and in its postproceedings in 2011, a further study of these parameters was made; in particular, the first parameter was applied to the functions F + L, where L is affine, providing more nonlinearity parameters. In 2010, motivated by the study of Costas arrays, two parameters called ambiguity and deficiency were introduced by Panario et al. for permutations over finite Abelian groups to measure the injectivity and surjectivity of the derivatives, respectively. These authors also studied some fundamental properties and cryptographic significance of these two measures. Further studies followed without comparing the second pair of parameters to the first one. In this paper, we observe that ambiguity is the same parameter as NBF up to additive and multiplicative constants (i.e., up to rescaling). We perform the necessary work of comparison and unification of the results on NBF and on ambiguity, which have been obtained in the five papers devoted to these parameters. We generalize some known results to any finite Abelian groups. More importantly, we derive many new results on these parameters.