6G network is expected to support a high number of devices, which require adopting decentralized architectures and lightweight protocols, to allow the systems to effectively meet the demands of such networks. However, the authentication is performed only after an initial connection has already been established, leaving the network exposed to unauthenticated devices during the initial phase. In this paper, we propose an edge-assisted trust establishment scheme for B5G/6G networks that shifts the pre-authentication process closer to the edge, particularly when establishing end-to-end communication with an external Application Function. It starts the trust establishment process from the initial access request sent by the User Equipment (UE). This enables the gNB to verify UE attributes before engaging in full authentication procedures. Such an approach will reduce the attack surface and unnecessary resource usage. The proposed scheme has been evaluated, and the results demonstrated that the proposed approach is both secure and efficient in edge nodes. A formal verification of the scheme has also been conducted using the TAMARIN prover, which confirmed its security guarantees.

Secret sharing is a general method for distributing sensitive data among the participants of a system such that only a collection of predefined qualified coalitions can recover the secret data. One of the most widely used special cases is threshold secret sharing, where every subset of participants of size above a given number is qualified. In this short note, we propose a general construction for a generalized threshold scheme, called conjunctive hierarchical secret sharing, where the participants are divided into disjoint levels of hierarchy, and there are different thresholds for all levels, all of which must be satisfied by qualified sets. The construction is the first method for arbitrary parameters based on finite geometry arguments and yields an improvement in the size of the underlying finite field in contrast with the existing results using polynomials.

In surrogate-assisted evolutionary optimization, privacy-preservation and trusted data sharing has become an increasingly important concern, especially in scenarios involving distributed sensitive data. Existing privacy-preserving surrogate-assisted evolutionary optimization algorithms heavily rely on the basic federated learning framework. However, recent findings have revealed possible vulnerabilities within this framework, including susceptibility to adversarial threats like gradient leakage and inference attacks. To address the above challenges and enhance privacy protection, this paper proposes to protect the raw data by applying a differentially private stochastic gradient descent method to train surrogate models. A differential evolution operator is designed to generate personalized new samples for multiple clients based on promising and additional auxiliary samples, avoiding the exposure of online newly generated data. Moreover, a similarity-based aggregation algorithm is integrated to effectively construct the global surrogate model. A rigorous security analysis is provided to further validate the effectiveness of the proposed method in privacy protection. Experimental results show that the proposed method exhibits remarkable optimization performance on a set of synthetic problems with federated settings while maintaining the data privacy.

Decentralized Attribute-based Encryption (DABE) is an extension of public key cryptography that allows the ciphertext to be decrypted by any node that has a predefined set of attributes. DABE can be used to control access to Internet of Things (IoT) devices and data based on attributes such as the type of device, the location, and the role of the user. Due to its heavy computation requirements, the DABE either can not be implemented on the weak devices or will be implemented with significant delay.In this paper, we study two distributed solutions of lightweight encryption in DABE using secret sharing and outsourcing. The analysis of our results showed that both approaches provided a lightweight encryption property of DABE. The secret sharing outperformed the outsourcing in small number of attributes, and both provided close efficient results in case of high number of attributes.

Abstract To achieve a fully connected network in Internet of Things (IoT) there are number of challenges that have to be overcome. Among those, a big challenge is how to keep all of the devices accessible everywhere and every time. In the IoT network, the assumption is that each IoT device can be reached by any client at any given time. In practice, this is not always possible and without a proper mechanism the nodes behind a NAT are unable to communicate with each other directly, and their addresses have to be shared through a trusted third party. This challenge becomes harder by taking into consideration that most NAT traversal approaches have been developed prior to rising of the IoT, without taking into account the constrained nature of the participating devices and mostly depend on a centralized entity. In this paper we proposed the Distributed Address Table (DAT), a decentralized, secure and lightweight address distribution model that allows any two nodes to get the addresses of the other end without relying on a trusted third party. Structured Peer-to-Peer (P2P) overlay by utilizing Distributed Hash Table (DHT) technique is generated as its underlying communication scheme to ensure that all participating devices are accessible at any given time. This is achieved through simple, yet secure and efficient decentralized model. The DAT adopts the edge/fog computing paradigms to ensure a decentralized address distribution. The results showed that the proposed model is efficient. In addition, the security properties of the proposed model have been defined and proved.

We consider the information ratio of graph based secret sharing schemes in a special case of graphs in which vertices of degree at least 3 are not connected by an edge. We prove that – after some trivial reduction of the original graph – the information ratio depends on the maximal value of the difference of the degree and the number of triangles containing a given vertex of the reduced graph. This result can be considered as a common generalization of previous results on the information ratio of special trees and graphs of girth at least 6.

Abstract In the history of secret sharing schemes many constructions are based on geometric objects. In this paper we investigate generalizations of threshold schemes and related finite geometric structures. In particular, we analyse compartmented and hierarchical schemes, and deduce some more general results, especially bounds for special arcs and novel constructions for conjunctive 2-level and 3-level hierarchical schemes.

Perfect secret sharing scheme is a method to distribute a secret information s among participants such that only predefined coalitions, called qualified subsets of participants can recover the secret, while any other coalitions, the unqualified subsets, cannot determine anything about the secret. The most important property is the efficiency of the system, which is measured by the information ratio. It can be shown that for graphs the information ratio is at most (δ + 1)/2 where δ is the maximum degree of the graph. Blundo et al. constructed a family of δ-regular graphs with information ratio (δ + 1)/2 on at least c · 6δvertices. We improve this result by constructing a significantly smaller graph family on c · 2δvertices achieving the same upper bound both in the worst and the average case.

The growing number of communicating devices in the Internet of Things (IoT) network requires an efficient resource discovery scheme without relying on centralized entity that may turn into a bottleneck affecting the system efficiency. In this paper we propose a distributed model for resource discovery in IoT. The model is based on structured peer-to-peer (p2p) scheme and follows the general system trend of fog computing. It supports multi-attribute queries and utilizes a Distributed Hash Table (DHT) as an overlay to organize the discovery process in a distributed manner. A specific method for identifier generation has been introduced to ensure the privacy of objects. Additionally, an address propagation model in the system reduces the overhead in the network by allowing the local lookup instead of global lookup for pre-connected objects. Our preliminary evaluation shows that the proposed model has a lower latency comparing to the cloud based resource discovery.