Within the context of the Industrial Internet, data has emerged as an increasingly critical asset in the domains of enterprise production, operation, and management, thereby solidifying its role as a pivotal determinant of competitive advantage for corporate entities. Nonetheless, the intrinsic characteristics of Industrial Internet data, characterized by its diverse origins, substantial volumes, and intricate privacy considerations, have given rise to issues surrounding the unambiguous establishment of data rights and inadvertent disclosure of sensitive information throughout the processes of data propagation and value transmission. To address these multifaceted challenges, this paper introduces a systematic framework for data rights verification, one that incorporates the utilization of Non-Fungible Tokens (NFTs) equipped with a secure locking mechanism in conjunction with adaptive federated learning. This proposed framework bifurcates the data rights verification process into two distinctive phases: data ownership verification and validation of data usage rights. Through the meticulous implementation of smart contracts and the adept utilization of the locking mechanism inherent within NFTs, the framework effectively dissects the intricacies of data rights, thereby ensuring contemporaneousness in data utilization and enabling controlled access. Furthermore, in the phase dedicated to the validation of data usage rights, we introduce an approach firmly rooted in the principles of adaptive federated learning. Leveraging the FedMGDA+ algorithm for distributed storage and computation, this approach successfully validates data usage rights. Empirical findings substantiate the significant advantages encapsulated within this framework, particularly in the domains of data and identity security. Notably, this framework maintains robust precision and scalability throughout the process of data rights verification for distributed model training, thereby exhibiting commendable performance in this intricate domain.

The Bitcoin system uses anonymous transactions to protect users’ privacy, but attackers can use this defect of bitcoin transactions to discover the association between bitcoin addresses. At present, address clustering methods can make use of these vulnerabilities to associate the address as an entity to a certain extent. However, these address clustering methods have problems such as an insufficient inference rate of change addresses, inability to identify mixing transactions, and low efficiency of algorithm implementation. We propose some solutions to these problems. 1) We improve the method of change address identification to identify and mark more of them. 2) We propose a heuristic address clustering method related to mixing transactions, which can identify their privacy vulnerabilities. 3) We propose an incremental address clustering method that can store the historical state and more quickly discover the anonymity defect of Bitcoin. We use real Bitcoin transaction data to demonstrate our method’s feasibility and reliability.

A key problem in any automatic software verification system is the inference of loop invariants. When analyzing program structures involving disjunctive semantics, abstract interpretation has the problem of precision loss. Thus, some techniques were proposed to decompose such loop structures into a semantically equivalent sequence of loops with conjunctive semantics whose invariants can be generated by abstract interpretation directly. However, these works assumed that the iteration processes of nested branches are separate without consideration of non-monotone loop structures where those interweave with each other. In order to solve this problem, we present a novel static analysis technique for non-monotone loops. It analyzes loop convergence condition and traces the transfer between nested branches of finite non-monotone loops. With analytical results, it generates the loop invariants with precise semantics. Meanwhile, it takes advantage of cyclical nature of result expressions to restrict search space and accelerate computation procedure. Finally, experimental results show the potential of our approach, which is also helpful for reasoning about certain program security properties.

With the universal application of software cluster systems, their reliability is drawing more and more attention from academia to industry. A cluster system is a kind of software load-sharing system (LSS) whose reliability is significantly dependent on system software. Therefore, traditional reliability analysis methods for hardware LSSs are not applicable for cluster systems. In this paper, we develop a reliability analysis model for redundant cluster systems consisting of initial servers and cold standby servers used to replace failed ones. System reliability process is modeled with a state-based non-homogeneous Markov process (NHMH), where each state corresponds to a non-homogeneous Poisson processe (NHPP). NHPP arrival rate is expressed using Cox's proportional hazard model (PHM) in terms of cumulative and instantaneous workload of system software. In addition to redundant cluster systems without repair, the model also can be extended to analyze those with restart. The analysis results are meaningful to support cluster management and design decisions. Finally, the evaluation experiments show the potential of our model.

Consider that a set of balls of n different colors are thrown into m bins with the assumption that the ball number of each color is constant and the number of balls in each bin is also constant. Our optimal goal is to find a feasible placement such that the distinct colors of remaining balls should be at least c after removing any k bins (k ≤ m) with the minimum number of balls. We present that the optimal colored bins in bins is equivalent to the optimization of Fractional Repetition (FR) codes in distributed storage systems. Here balls correspond to coded packets and bins correspond to storage nodes. This problem can be represented as biregualr graph and then deduced to the Zarankiewicz problem, which is a well-known extremal graph theoretic problem. We present the problem with the relation to combinatorial design theory, especially t-designs and propose the explicit construction algorithm for the optimization problem from t-designs. Some constructions of the optimized FR codes by 2-designs are analyzed to tolerate the desired k fault-tolerance with c = n - 1.

To ensure high reliability and storage efficiency, erasure codes are preferred in storage systems. With the prevalent of distributed storage systems such as clouds storage, how to design a scalable and efficient erasure-resilient code is challenging. We propose a scalable binary linear code to keep up with data growth which has the following properties. Given the group size k and the code block length n, the proposed code corrects any two bit erasures among the n bits. The redundancy overhead of the code is 2/(k + 2), and each data bit affects exactly 2 parity bits. As results of these properties, if a data bit is changed or added, only two parity bits need to be updated; and the recovery of an erasured bit requires accessing at most k other bits and the recovery of two erasured bits requires at most 2k other bits. We give the construction algorithm by the order expansion of regular graphs; moreover, we optimize the failure resilience during the construction procedure. Compared with existing codes, our proposed code has notable benefits in storage scalability, redundancy overhead and I/O bandwidth. The deployment of the proposed code in distributed storage systems can be simple and practical.

In this paper, we study binary pattern erasure codes, i.e., binary codes that are resiliant to erasures from a family P of possible erasures. We give an algorithmic proof of the existence of a binary linear code with codewords of length n that is resiliant to erasures from P when P satisfies the properties: every pattern p ϵ P has size m and every letter in the alphabet occurs in at most c patterns. The density of the parity matrix is plays a important role in storage applications, so we also introduce a new low density code basing on graph theory.

Heterogeneity is the natural feature in distributed networks. Different from the traditional disk array, the amount of data allocated on heterogenous peers may be not the same. To maximize the reliability of stored data objects in heterogeneous networks, the optimal allocation of erasure-coded fragments is a challenging problem constrained with heterogeneous peer availabilities and redundancy overhead. This paper examines this optimal problem considered MDS erasure codes applied into distributed storage networks. First, we model the reliability of an allocation with the weighted-k-out-of-s model and extend its properties to efficiently calculate the reliability of an allocation; then we reduce the reliability computation of a given allocation to linear computation cost based on the weighted k-out-of-s model. Then, we deduce the problem to integer partition problem and propose two order-based search algorithms. Our experiments show that our proposed algorithms can be applied to find the optimal allocations efficiently in various practical coding cases. Furthermore, we evaluate the performance of our proposed search algorithms with some practical storage settings, and then present experimental results including the reliability, redundancy overheads and allocation pattern for the optimal allocation driven by practical network traces.

Security is a fundamental prerequisite for the survivability and reliability of wireless networks. In a network where limited wireless resources have to be shared, selfish nodes can manipulate relevant network parameters to gain more access to the resources, and hence obtain a higher performance than their fair share, while the performance of well-behaved nodes will be significantly degraded. This paper considers the environment of an IEEE802.11 WLAN, and proposes a solution from the prospective of a principal-agent system. Our solution uses an incentive and a constraint mechanism to encourage the selfish agent to perform normally. Our method does not modify the IEEE 802.11 protocol, but requires an additional principal node only. Simulation results show that our method can overcome the influence of selfish nodes improve the network fairness performance while maintaining the throughput performance.