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Public key encryption with equality test (PKEET) is a cryptographic primitive that enables a tester to determine whether two ciphertexts encrypted with same or different public keys have been generated from the same message without decryption. Previous studies extended PKEET to public key encryption with designated-position fuzzy equality test (PKE-DFET), enabling testers to verify whether plaintexts corresponding to two ciphertexts are equal while ignoring specific bits at designated positions. In this work, we have filled the research gap in the identity-based encryption (IBE) cryptosystems for this primitive. Furthermore, although our authorization method is the all-or-nothing (AoN) type, it overcomes the shortcomings present in the majority of AoN-type authorization schemes. In our scheme, equality tests can only be performed between a ciphertext and a given plaintext. Specifically, even if a tester acquires multiple AoN-type authorizations, it cannot conduct unpermitted equality tests between users. This significantly reduces the risk of user privacy leaks when handling sensitive information in certain scenarios, while still retaining the flexible and simple characteristics of AoN-type authorizations. We use the Chinese national cryptography standard SM9-IBE algorithm to provide the concrete construction of our scheme, enhancing the usability and security of our scheme, while making deployment more convenient. Finally, we prove that our scheme achieves F-OW-ID-CCA security when the adversary has the trapdoor of the challenge ciphertext, and achieves IND-ID-CCA security when the adversary does not have the trapdoor of the challenge ciphertext. [ABSTRACT FROM AUTHOR]

In the last decade, E-voting has received great attention due to its advantages in efficiency and accuracy. Fan et al. presented a novel E-voting system named HSE-Voting by utilizing homomorphic signcryption. The HSE-Voting system was claimed to gain a provable security goal under the standard proof. In this paper, we illustrate that their scheme may suffer from some potential security issues. On the one hand, the voting information could be recovered by the authentication center (AC). On the other hand, any malicious voter could disrupt the voting system undetected by locally modifying his ballot. In order to increase the resilience of the voting system to risks, an improvement of the HSE-Voting system is developed. Our improved system fixes the above security weaknesses but increases the computation cost on the AC side by a small amount. In addition, the proposed scheme satisfies voter anonymity, ballot privacy, and verifiability of election results. [ABSTRACT FROM AUTHOR]

Public key encryption with equality test (PKEET) is a cryptographic primitive that enables a tester to determine, without decryption, whether two ciphertexts encrypted with different public keys generate from the same message. In previous research, public key encryption with equality test (PKEET) was extended to include identity-based encryption with equality test (IBEET), thereby broadening the application of PKEET. Subsequently, certificateless encryption with equality test (CLEET) was introduced to address the key escrow problem in IBEET. However, existing CLEET schemes suffer from inefficiency and potential information leakage when dealing with multiple ciphertexts due to the need for pairwise equality tests. To address this issue, we propose a concept of certificateless encryption supporting multi-ciphertext equality test with proxy-assisted authorization (CLE-MET-PA). CLE-MET-PA incorporates the functionality of the multi-ciphertext equality test into CLEET, enabling a tester to perform a single equality test on multiple ciphertexts to determine whether the underlying plaintexts are equal, without revealing any additional information. This enhances the security of our scheme while significantly reducing the computational overhead compared to multiple pairwise equality tests, making our scheme more efficient. Additionally, our approach integrates proxy-assisted authorization, allowing users to delegate a proxy to grant authorizations for equality tests on their behalf when offline. Importantly, the proxy token used in our scheme does not include any portion of the user's private key, providing enhanced protection compared to traditional PKEET schemes in which the user token is often part of the user's private key. We construct a concrete CLE-MET-PA scheme and prove that it achieves CPA security and attains CCA security through an FO transformation. [ABSTRACT FROM AUTHOR]

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts image analysis technology based on artificial intelligence and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and effectively deal with problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users' private information from the provided data and mistakenly execute aggregation protocols to forge aggregation results. In order to ensure the reliability of the federated learning scheme, we must protect the privacy of users' information and ensure the integrity of the aggregation results. This paper proposes an effective secure aggregation verifiable federated learning scheme, which has both high communication efficiency and privacy protection function. The scheme encrypts the gradients with a single mask technology to securely aggregate gradients, thus ensuring that malicious servers cannot deduce users' private information from the provided data. Then the masked gradients are hashed to verify the aggregation results. The experimental results show that our protocol is more suited for bandwidth-constraint and offline-users scenarios. [ABSTRACT FROM AUTHOR]

In the data era, to simultaneously relieve the heavy computational burden of mining data information from data owners and protecting data privacy, privacy-preserving frequent itemset mining (PPFIM) is presented and has attracted much attention. In PPFIM, data owners and miners outsource the complex task of data mining to the cloud server, which supports strong storage and computing power, and the cloud server cannot extract additional data privacy other than that which is shown by data owners or miners. However, most existing solutions assume that cloud servers will honestly perform the mining process and return the correct results, whereas cloud services are usually provided by a charging third party that may in practice return incorrect results due to computation errors, malicious or criminal activities, etc. To solve this problem, in this paper, we present a verifiable PPFIM protocol on vertically partitioned databases to support the verifiability of the integrity of the mining results, where data owners can authorize the cloud server to perform federated mining on vertically partitioned databases without leaking data information and detect dishonest behaviors in the cloud server from the returned results. We adopt a dual cloud setting to enable data owners to be offline after uploading their encrypted databases to the cloud server, which further relieves the burden on data owners. We implement our protocol and give a detailed analysis in terms of verification accuracy, which shows that the dishonest behaviors of the cloud server can be detected with a probability close to 1 and a sacrifice of only a 1 % increase in database size. [ABSTRACT FROM AUTHOR]

As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning schemes based on local differential privacy (LDP) ignore an important issue: they do not consider each client's differentiated privacy requirements. This paper introduces a perturbation algorithm (PDPM) that satisfies personalized local differential privacy (PLDP), resolving the issue of inadequate or excessive privacy protection for some participants due to the same privacy budget set for all clients. The algorithm enables clients to adjust the privacy parameters according to the sensitivity of their data, thus allowing the scheme to provide personalized privacy protection. To ensure the privacy of the scheme, we have conducted a strict privacy proof and simulated the scheme on both synthetic and real data sets. Experiments have demonstrated that our scheme is successful in producing high-quality models and fulfilling the demands of personalized privacy protection. [ABSTRACT FROM AUTHOR]

Although cloud storage provides convenient data outsourcing services, an untrusted cloud server frequently threatens the integrity and security of the outsourced data. Therefore, it is extremely urgent to design security schemes allowing the users to check the integrity of data with acceptable computational and communication overheads. In this paper, we first propose a public data integrity verification scheme based on the algebraic signature and elliptic curve cryptography. This scheme not only allows the third party authority deputize for users to verify the outsourced data integrity, but also resists malicious attacks such as replay attacks, replacing attack and forgery attacks. Data privacy is guaranteed by symmetric encryption. Furthermore, we construct a novel data structure named divide and conquer hash list, which can efficiently perform data updating operations, such as deletion, insertion, and modification. Compared with the relevant schemes in the literature, security analysis and performance evaluations show that the proposed scheme gains some advantages in integrity verification and dynamic updating. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, massive data are transferred to cloud servers for storage and management savings. For privacy concerns, data should be encrypted before being uploaded. In the encrypted-domain (ED), however, many data computing methods working in the plain-domain are no longer applicable. Data retrieval has become a significant obstacle to cloud storage services. To break through this limitation, we propose a multi-keyword searchable encryption scheme by introducing probability trapdoors. Firstly, a keywords probability trapdoor is established to ensure that the scheme can resist indistinguishable attacks. Based on the keywords trapdoor, we present the keywords vector to make the scheme realize multi-keyword search in the process of data retrieval in the ED. Both security and performance analysis confirm the advantages of the proposed scheme in terms of search functionality and complexity. [ABSTRACT FROM AUTHOR]

By introducing an easy knapsack-type problem, a probabilistic knapsack-type public key cryptosystem (PKCHD) is proposed. It uses a Chinese remainder theorem to disguise the easy knapsack sequence. Thence, to recover the trapdoor information, the implicit attacker has to solve at least two hard number-theoretic problems, namely integer factorization and simultaneous Diophantine approximation problems. In PKCHD, the encryption function is nonlinear about the message vector. Under the re-linearization attack model, PKCHD obtains a high density and is secure against the low-density subset sum attacks, and the success probability for an attacker to recover the message vector with a single call to a lattice oracle is negligible. The infeasibilities of other attacks on the proposed PKCHD are also investigated. Meanwhile, it can use the hardest knapsack vector as the public key if its density evaluates the hardness of a knapsack instance. Furthermore, PKCHD only performs quadratic bit operations which confirms the efficiency of encrypting a message and deciphering a given cipher-text. [ABSTRACT FROM AUTHOR]