The development of E-commerce platforms has provided convenience for consumers, and many shopping platforms advertise products to users based on their preferences. However, this kind of recommendation, based on historical browsing or purchase data, can infer user behavior, which threatens user privacy. Encrypted browsing or purchase data protects user privacy but blinds the server from making recommendations. We have improved the algorithm (GPKE-MET) based on equivalence testing for public key encryption to support batch testing, which can be applied to large-scale encrypted data processing on E-commerce platforms. In addition,GPKE-METis against guessing by the attackers (i.e., malicious cloud server) for the predictable information so as to enhance the data privacy. Therefore, with the help ofGPKE-MET, the E-commerce platform can make product recommendations effectively, although the underlying data is encrypted.

Secure deduplication not only optimizes cloud storage but also prevents data leakage. However, traditional schemes are with high computation and communication costs to deal with large-scale multimedia data. To address this problem, we propose a fog computing-based fuzzy deduplication (HDeFC), a two-stage privacy-preserving deduplication protocol. Our approach prevents guessing attacks and single point of failure, besides that also implements noninteractive Proof of Ownership and label consistency verification. In addition, we also achieve the access permission for only legitimate users. Our performance evaluation shows that the hierarchical setting is efficient in deduplication.

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore.

Huge amounts of data from various sources are substantial to dependable distributed machine learning, especially for trustworthy federated learning (FL). However, existing FL methods are difficult to collect enough data for training the global model more accurately, especially in cross-device scenarios. In this paper, we propose a new federated generative adversarial network empowered by differential privacy and knowledge transfer named Fed-GAN, which can be used to address the problem of data shortage and prevent generator leakage from resource-constrained devices, as well as generate high-quality synthetic data while ensuring strict DP guarantees. Different from other generative model methods, our Fed-GAN framework can achieve efficient and secure generative model training and limited permission for resource-constrained devices to prevent them from leaking or misusing the generator. In addition, we propose a pHash-KT method for our Fed-GAN framework, which selects potentially high-quality data through the knowledge of each client for improving the utility of synthetic data. Our FedGAN framework satisfies ($\frac{2kJ\lambda}{\sigma^2}+\frac{log 1/\delta}{\lambda-1},\delta}$)-DP, and also has high resistance when number of adversaries is 10%-70% of the total number of clients. Extensive experiments demonstrate that our Fed-GAN framework not only generates high-quality synthetic data, but also provides strict DP guarantees, compared with other generative model methods. Our code is publicly available at https://github.com/daxx1/fed-gan

Fuzzy deduplication frees up more storage space than exact deduplication. Traditional deduplication schemes do not only fail to extend to fuzzy deduplication directly but also be vulnerable to brute-force guessing attacks (e.g. Convergent Encryption (CE) and Message-locked Encryption (MLE)). Currently, fuzzy deduplication is mainly handled with the help of aided-server and a third-party validator for the security and feasibility. This work extracts a single-server fuzzy deduplication scheme for encrypted multimedia data under dual-entity (e.g., uploader & server, and a third-party is not necessary). In addition, we perform experimental evaluation of DEFD on real-world datasets. The results show that DEFD can save 95+% of storage space, and for the real outsourced data deduplication scenarios DEFD can improve the accuracy by 5.287%.

Cloud storage technology is constantly evolving, resulting in a significant amount of duplicate data being stored in the cloud, particularly multimedia data such as images and videos. In terms of data privacy and storage optimization, the encrypted deduplication should be checked to save space overhead for cloud servers. Compared to exact deduplication, fuzzy deduplication is low-cost for encrypted multimedia data. Focusing on reducing the false deletion rate, an efficient and secure fuzzy deduplication system based on dual-feature without additional servers is proposed. We also propose a concept of pre-verification for label consistency to compensate for the loss that cannot be fixed through post-verification. Therefore, it is more practical. Finally, we conduct experiments on real-world datasets for performance evaluation. The experimental results show good performance in terms of both computational cost and deduplication efficiency.

Trigonometric functions are crucial non-linear operations used in scientific computation and complex machine learning models. However, existing secure computing frameworks either lack support for these operations, or suffer from undesirable performance bottleneck. In this paper, we present an efficient and precise fixed-point framework called TriFSS for securely evaluating trigonometric functions. Specifically, we first design new building blocks based on advanced Function Secret Sharing techniques, achieving reduced communication and computation overhead. Second, with these efficient components, we propose a general evaluation process for these functions, in which periodic properties are fully exploited for better performance. Moreover, we implement the TriFSS framework and conduct extensive experiments. The experimental results show that our protocols achieve at least 23x less communication overhead and 2.8x less latency than the state-of-the-art frameworks, while only resulting in 1 ULP error, which is comparable to floating-point based works.

VANETs need secure communication. Authentication in VANETs resists the attack on the receipt of false information. Authenticated group key agreement (GKA) is used to establish a confidential and authenticated communication channel for the multiple vehicles. However, authentication incurs privacy leakage, that is, by using digital signature. Therefore, the deniability is deserved for GKA (which is termed as DGKA) due to the privacy protection. In the DGKA protocol, each participant interacts with intended partners to establish a common group session key. After this agreement session, each participant can not only be regarded as the intended sender but also deny that it has ever participated in this session. Therefore, under this established key, vehicles send confidential messages with authentication property and the deniability protects the vehicles privacy. We present a novel transformation from an unauthenticated group key agreement to a deniable (authenticated) group key agreement without increasing communication round. Our full deniability is achieved even in the concurrent setting which suits the Internet environment. In addition, we design an authenticated and privacy‐preserving communication protocol for VANETs by using the proposed deniable group key agreement.

Abstract Ring signature scheme protects the privacy while signer is signing. In the ring signature scheme, the signer can randomly choose verification keys of entities and generate a signature on behalf of these entities. The generated signature can be verified by anyone by inputting all these verification keys. Consequently, a ring signature convinces a verifier that one member from these entities produces this signature without revealing which one. This property is good for the signer as his identity is not leaked. However, the signer also can make use of this capacity to generate a malicious signature on behalf of a ring. Because of the unconditional anonymity of ring signature, this signer cannot be traced to be responsible for his malicious signing. Group signature can avoid this problem because the group manager in the group signature can trace the actual signer by using the trapdoor. However, the group is fixed from the beginning and it needs a complicated setup algorithm. Deniable ring signature was introduced by Komano et al., which allows to revoke the anonymity of actual signer without the manager's help if necessary. The actual signer can confirm his signing for anyone through the confirmation protocol. On the other hand, non‐signers in the ring can disavow this signing by the disavowal protocol. Therefore, the actual signer can be traced. However, Komano's scheme was proven in random oracles, and the traceability protocols (confirmation and disavowal protocols) are interactive. To improve Komano's construction, this work proposes a new efficient non‐interactive deniable ring signature scheme in the standard model. It is a kind of ring signature and therefore, it does not require a setup algorithm, and the ring in the scheme is flexible. Copyright © 2013 John Wiley & Sons, Ltd.

Conditionally anonymous ring signatures are a variant of ring signatures such that the anonymity is conditional: if a user is the true signer, then he can claim this through a confirmation protocol; if he is not the signer, he can prove this through a disavowal protocol. Hence, this can preserve the anonymity of a signer while reserving the right to trace it when necessary. The security of such a signature also requires that an innocent non-signer will not be framed as a signer. In this paper, we propose a new framework for this type of signature without random oracles. Our construction can be realized under general complexity assumptions and has a simple structure. In contrast, previous works are based on non-standard assumptions or proved secure in the random oracle model.

The members in the dynamic peer group (DPG) are divided into several clusters according to linker cluster architecture (J.B. Dennis and E. Anthony, 1981). In each cluster, the members perform the BD protocol (M. Burmester and Y. Desmedt, 1994) to establish the cluster key and the TGDH protocol (Y. Kim et al., 2004) is performed among the clusters to generate the group key. This method can not only avoid the weakness of AKA (auxiliary key agreement) of BD, but also reduce the computation cost of TGDH scheme. In our scheme the members have been authenticated based identity, and we prove the security in random oracle model.