Threshold private set intersection (TPSI) allows multiple parties to learn the intersection of their input sets only if the size of the intersection is greater than a certain threshold. This task has been demonstrated useful with practical applications, and thus many active research has been conducted. However, current solutions for TPSI are still slow for large input sets e.g., n=2^20 for the set size, and the potentially practical candidates are only secure against semi-honest adversaries. For the basic PSI, there have been efficient and scalable solutions, even in the malicious settings. It is interesting to determine whether adding a threshold feature would inherently incur a large overhead to PSI.

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems.

Education on machine learning and data science has drawn a lot of attention in both higher education and vocational training. Although various tools and services such as Jupyter Notebook and Google Cloud's AI have been developed for building and training models, they are not suitable for direct use in educational settings. For example, teachers expect a platform where they can easily distribute and grade programming assignments, and students want to quickly start coding and training models without the burden of setting up an environment. To this end, we develop MLadder, an online training system for machine learning and data science education. Specifically, we seamlessly integrate two open-source software, CodaLab and Jupyter Notebook, which are used for hosting assignments and building models, respectively. Moreover, we devise several methods to make the system lightweight and scalable, so that it can be deployed on-premises even with limited resources. We have used MLadder in the machine learning and data science courses in our school and facilitated both teaching and learning.

Electric Vehicle Routing Problem (EVRP) is considerably challenging due to the capacity and electricity constraints of electric vehicles (EVs). Most existing studies on EVRP consider no limits on charging times when optimizing the routes of EVs. However, due to the long time of charging, the charging times of EVs are usually limited due to the urgent service demands of customers. To simulate this practical problem, this paper first formulates the EVRP with both capacity and charging time constraints (EVRP-CC). To tackle this new optimization problem, this paper further devises a two-stage solution construction method for ant colony optimization (ACO) to build feasible solutions to EVRP-CC. Subsequently, we embed the proposed method into five popular and classical ACO algorithms, namely ant system (AS), ranking based ant system (Rank-AS), elite ant system (EAS), max-min ant system (MMAS), and ant colony system (ACS), to solve EVRP-CC. Extensive experiments conducted on several instances generated from the widely used EVRP benchmark set demonstrate that the proposed solution construction method is effective to help ACO to solve EVRP-CC. In particular, Rank-AS with the proposed solution construction method achieves the best overall performance in solving EVRP-CC.

Secret sharing is a basic tool in modern communication, which protects privacy and provides information security. Among the secret sharing schemes, fairness is a vital and desirable property. To achieve fairness, the existing secret sharing schemes either require a trusted third party or the execution of a multiround protocol, which are impractical. Moreover, the classic scheme requires expensive computing in the secret verification phase. In this work, we provide an outsourcing hierarchical threshold secret sharing (HTSS) protocol based on reputation. In the scheme, participants from different levels can fairly reconstruct the secret, and the protocol only needs to run for one round. A cloud service provider (CSP) uses powerful computing resources to help participants complete homomorphic encryption and complex verification operations, and the CSP cannot be aware of any valuable information. The participants can obtain the secret with a small number of operations. To avoid collusion, we suppose that participants have their own reputation value, and they are punished or rewarded according to their behavior. The reputation value of a participant who deviates from the protocol will decrease; therefore, the participant will choose a cooperative strategy to obtain better payoffs. Lastly, our scheme is proved to be secure, and experiments indicate that our scheme is feasible and efficient.

Secret sharing is an important component of cryptography protocols and has a wide range of practical applications. However, the existing secret sharing schemes cannot apply to computationally weak devices and cannot efficiently guarantee fairness. In this study, a novel outsourcing secret sharing scheme is proposed. In the setting of outsourcing secret sharing, clients only need a small amount of decryption and verification operations, while the expensive reconstruction computation and verifiable computation can be outsourced to cloud service providers (CSP). The scheme does not require complex interactive argument or zero‐knowledge proof. The malicious behaviour of clients and CSP can be detected in time. Moreover, the CSP cannot get any useful information about the secret, and it is fair for every client to obtain the secret. At the end of this study, the authors prove the security of the proposed scheme and compare it with other secret sharing schemes.

The first joint fingerprinting and decryption (JFD) for vector quantization (VQ) images addressed the problem that the decrypted multimedia data may be redistributed from authorized customers to unauthorized customers. The scheme also caused conventional JFD methods to be equipped with a special ability to resist noise interference. Till now, some existing schemes related have been proposed to protect the multimedia content and distribution, but these schemes failed to tackle several problems existing in the original JFD scheme based on VQ image, including high transmission cost and severe fingerprinted image distortion. In this paper, we propose a novel JFD method by combining a weight-sum function with fingerprinting embedding and extraction for VQ images. Under the combination, the visual quality of the fingerprinted image is further improved; also the fingerprint extraction implements a blind extraction process. Experiments and analyses demonstrate the feasibility of the proposed method.

With the wide application of Internet of Things (IoT), a huge number of data are collected from IoT networks and are required to be processed, such as data mining. Although it is popular to outsource storage and computation to cloud, it may invade privacy of participants’ information. Cryptography‐based privacy‐preserving data mining has been proposed to protect the privacy of participating parties’ data for this process. However, it is still an open problem to handle with multiparticipant’s ciphertext computation and analysis. And these algorithms rely on the semihonest security model which requires all parties to follow the protocol rules. In this paper, we address the challenge of outsourcing ID3 decision tree algorithm in the malicious model. Particularly, to securely store and compute private data, the two‐participant symmetric homomorphic encryption supporting addition and multiplication is proposed. To keep from malicious behaviors of cloud computing server, the secure garbled circuits are adopted to propose the privacy‐preserving weight average protocol. Security and performance are analyzed.