Federated learning (FL) has emerged as a promising approach for collaborative machine learning while respecting data privacy. However, directly aggregating local models during FL training can expose sensitive information, as adversaries may analyze model parameters to infer the underlying raw data. To address this concern, implementing a robust identity authentication mechanism can verify the integrity of local models and confirm their sources. However, many existing authentication schemes are computationally intensive, which can hinder performance. In this article, we present a lightweight authentication scheme for FL called EPPAFL. This innovative approach is compatible with all machine learning algorithms, maintains data utility, and ensures strong privacy protection. Additionally, it can be integrated with detection mechanisms to defend against data poisoning and free-rider attacks. Specifically, we first design a certificateless signature scheme based on the elliptic curve cryptography (ECC) and then propose an anonymous and efficient authentication protocol based on the Chinese remainder theorem (CRT), which supports batch verification, dynamic updates, and revocations of group keys. Moreover, participants interact anonymously to achieve secure communication, and trusted authority (TA) can track and recover the real identities of participants in case of malicious activities. We provide formal security proofs and analyses to demonstrate the achieved security properties. Performance evaluations demonstrate that our proposed protocol significantly reduces the costs associated with signature generation and verification, outperforming the existing schemes in both communication and computational efficiency.

In this paper, a family of [q2- 1, 4, q2- q - 2] cyclic codes over Fqmeeting the Griesmer bound is presented. Their duals are [q2- 1, q2- 5,4] almost MDS codes and are optimal with respect to the sphere-packing bound. The q0-ary subfield codes of this family of cyclic codes are also investigated, where q0is any prime power such that q is power of q0. Some of the subfield codes are optimal and some have the best known parameters. It is shown that the subfield codes are equivalent to a family of primitive BCH codes and thus the parameters of the BCH codes are solved. The duals of the subfield codes are also optimal with respect to the sphere-packing bound. A family of [q2, 4, q2- q - 1] linear codes over Fqmeeting the Griesmer bound is presented. Their duals are [q2, q2- 4, 4] almost MDS codes and are optimal with respect to the sphere-packing bound. The q0-ary subfield codes of this family of linear codes are also investigated, where q0is any prime power such that q is power of q0. Five infinite families of 2-designs are also constructed with three families of linear codes of this paper.

Expert finding (EF) task has received widespread attention as an important task of information retrieval.One key category of EF is expert finding on the web, which seeks to rank influential public figures from diverse webpage sources with respect to given query.Previous web expert finding approach relies on casting webpages to hypergraph structure and run heat diffusion process to find the top ranking person vertices according to their heat of popularity.Such approach suffer from two major drawbacks:First, previous web expert finding approach (CoDiffusion) suffers from unflexibility of selecting queries.This means that all corresponding queries must be stored as vertices in hypergraph index beforehand, otherwise CoDiffusion cannot run the expert finding process.Such defect make it ungeneric and incapable of handling the newly invented technical terms or phrases in real world scenarios.Second, the performance of previous approach is less satisfying.We incorporate semantic relatedness information with Hypergraph Learning Framework and Affinity Propagation ({HLFAP}) to handle the above drawbacks.In order to overcome the first disadvantage, we distribute initial heat to the related vertices according to their semantic similarity on given query. In order to solve the second disadvantage, we propose semantic labeled hypergraph learning framework and person influence affinity propagation model to make high quality candidates can receive more heat transition. Experimental results shows that our generic methodology achieves more satisfying results than the non-semantics state-of-the-art baseline method.

WG-8 is a new lightweight variant of the well-known Welch-Gong (WG) stream cipher family, and takes an 80-bit secret key and an 80-bit initial vector (IV) as inputs. So far no attack on the WG-8 stream cipher has been published except the attacks by the designers. This paper shows that there exist Key-IV pairs for WG-8 that can generate keystreams, which are exact shifts of each other throughout the keystream generation. By exploiting this slide property, an effective key recovery attack on WG-8 in the related key setting is proposed, which has a time complexity of 253.32and requires 252chosen IVs. The attack is minimal in the sense that it only requires one related key. Furthermore, we present an efficient key recovery attack on WG-8 in the multiple related key setting. As confirmed by the experimental results, our attack recovers all 80 bits of WG-8 in on a PC with 2.5-GHz Intel Pentium 4 processor. This is the first time that a weakness is presented for WG-8, assuming that the attacker can obtain only a few dozen consecutive keystream bits for each IV. Finally, we give a new Key/IV loading proposal for WG-8, which takes an 80-bit secret key and a 64-bit IV as inputs. The new proposal keeps the basic structure of WG-8 and provides enough resistance against our related key attacks.