Existing privacy-preserving multi-keyword retrieval schemes often suffer from reduced retrieval efficiency, lack robust verification mechanisms in dynamic environments, and are prone to symmetric key leakage issues. To address these shortcomings, we propose a dynamic and secure multi-keyword search scheme with a two-factor verification mechanism, named Mimi. Specifically, Mimi first constructs a dynamic verification tree structure to accelerate the verification of the correctness of returned results. Second, it builds an encrypted searchable index that supports sub-linear search time complexity. Third, Mimi incorporates a secure symmetric key exchange protocol to protect the confidentiality of the symmetric key. Furthermore, Mimi supports multi-user search operations without increasing the index construction costs and accommodates dynamic updates to both user roles and data. Through comprehensive security analysis, we demonstrate that Mimi ensures the security of the encrypted searchable inverted index and maintains query indistinguishability for users. Empirical evaluations show that the Mimi scheme is efficient and effective.

Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by aggregating local gradients from clients without sharing their training data. Despite its merits, it suffers from privacy concerns due to the leakage of local gradients. A popular approach is to have clients mask their local gradients using homomorphic encryption (HE). However, this not only results in a reliance on a trusted third party (TTP), but also leads to significant computational and communication overhead. In addition, in existing cross-silo FL protocols, the aggregation operation is performed by a centralized aggregator, raising new security issues. One of these issues involves verifying the correctness of the aggregated results returned by the aggregator. The aggregator has been removed in the cross-device setting by leveraging blockchain technology, but not in the cross-silo setting. In this paper, we propose FreeFL, an efficient privacy-preserving cross-silo FL that eliminates the need for the TTP and aggregator, as well as achieves the optimal communication rounds. The high-level idea behind FreeFL is to customize alightweightdecentralized symmetric encryption with additive homomorphism for cross-silo FL. To this end, we design an efficient decentralized multiparty symmetric encryption (DMSE) scheme and twolightweightmultiparty computation protocols. We evaluate the performance of FreeFL, and the experimental results indicate that FreeFL exhibits high efficiency in both computation and communication. Additionally, we conduct experimental comparisons between FreeFL and other existing HE-based cross-silo FL protocols to show that FreeFL achieves significant computational efficiency improvements.

The privacy-preserving federated learning schemes based on the setting of two honest-but-curious and non-colluding servers offer promising solutions in terms of security and efficiency. However, our investigation reveals that these schemes still suffer from privacy leakage when considering model poisoning attacks from malicious users. Specifically, we demonstrate that the privacy-preserving computation process for defending against model poisoning attacks inadvertently leaks privacy to one of the honest-but-curious servers, enabling it to access users' gradients in plaintext. To address this issue, we propose an enhanced privacy-preserving and Byzantine-robust federated learning (PBFL) framework that simultaneously achieves privacy, robustness, and efficiency. Central to our design is a novel Byzantine-tolerant aggregation strategy that defends against both conventional and adaptive poisoning attacks. It integrates normalization judgment, cosine similarity computation, and adaptive user weighting, with a dual-scoring trust mechanism and outlier suppression for stealthy attacks. In addition, we develop two privacy-preserving subroutines, namely secure normalization judgment and secure cosine similarity measurement, which operate over encrypted gradients using a trapdoor fully homomorphic encryption (FHE) scheme, ensuring both confidentiality and robust aggregation correctness. Theoretical analyses confirm that our scheme guarantees security, convergence, and efficiency even with malicious users and one malicious server. Extensive experiments demonstrate that our method effectively breaks prior privacy attacks, maintains high accuracy under diverse poisoning strategies, and significantly reduces computation and communication overhead compared to state-of-the-art PBFL schemes.

Existing CNN inference frameworks based on FHE often suffer from reduced efficiency and accuracy due to the polynomial approximation of activation functions, and they lack effective mechanisms to prevent sensitive information leakage during the final classification stage. To address these limitations, we propose FSAT, a fast and secure inference framework enhanced with adversarial training. Specifically, FSAT employs a private CNN model architecture, where linear layers are computed through an optimized homomorphic ciphertext convolution operation, while non-linear layer operations are efficiently realized using a secure searchable index and an encrypted look-up table, which replace polynomial activation approximations and significantly improve inference accuracy and latency performance. To further mitigate information leakage, we introduce a dual-constraint adversarial training scheme that makes it substantially more difficult for an adversary to infer sensitive attributes of the input data. Experimental results demonstrate that FSAT achieves high inference accuracy and efficiency while substantially reducing the risk of sensitive data leakage.

Cross-silo federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing training data, but privacy in FL remains a major challenge. Techniques using homomorphic encryption (HE) have been designed to solve this but bring their own challenges. Many techniques using single-key HE (SKHE) require clients to fully trust each other to prevent privacy disclosure between clients. However, fully trusted clients are hard to ensure in practice. Other techniques using multi-key HE (MKHE) aim to protect privacy from untrusted clients but lead to the disclosure of training results in public channels by untrusted third parties, e.g., the public cloud server. Besides, MKHE has higher computation and communication complexity compared with SKHE. We present a new FL protocol ESAFL that leverages a novel efficient and secure additively HE (ESHE) based on the hard problem of ring learning with errors. ESAFL can ensure the security of training data between untrusted clients and protect the training results against untrusted third parties. In addition, theoretical analyses present that ESAFL outperforms current techniques using MKHE in computation and communication, and intensive experiments show that ESAFL achieves approximate$204\times$−$953\times$and$11\times$−$14\times$training speedup while reducing the communication burden by$77\times$−$109\times$and$1.25\times$−$2\times$compared with the state-of-the-art FL models using SKHE.

Federated learning (FL) emerges as a promising collaborative framework within the field of machine learning (ML), offering the potential to train ML models on sensitive real-world data while maintaining data privacy. The primary security concerns surrounding FL, particularly the protection of local gradients’ privacy and ensuring the correctness of aggregated gradients, have attracted growing attention in both industry and academia. Recently, Duet al. proposed SVHFL, a secure and verifiable hybrid FL system. In this context, the term “secure” means that SVHFL functions as a privacy-preserving federated learning (PPFL) system, capable of protecting the privacy of local gradients from being learned by the server (i.e., the aggregator) and the clients, and “verifiable” implies that SVHFL can achieve the aggregation verification, i.e., it guarantees the correctness of aggregated gradients returned by the server. However, in this article, we propose two attacks that compromise SVHFL, demonstrating that SVHFL cannot protect the privacy of local gradients from being learned by the server and the clients. We analyze the internal causes of these privacy breaches in SVHFL and propose alternative solutions to prevent such privacy leaks. We hope that the exposure of these security vulnerabilities will act as a catalyst to prevent similar incidents from occurring in the future design of PPFL systems.

The secure and privacy-preserving federated learning scheme, NSPFL, aims to safeguard data privacy while also auditing data integrity. The solution provided by this scheme is highly novel. However, NSPFL has significant design shortcomings in terms of both privacy protection and data integrity verification. This work identifies specific issues within NSPFL and proposes effective countermeasures. Furthermore, our proposed solution can serve as a general approach for privacy-preserving multiparty computations, safeguarding privacy while enhancing efficiency.

The increasing privacy concerns associated with cloud-assisted image retrieval have captured the attention of researchers. However, a significant number of current research endeavors encounter limitations, including suboptimal accuracy, inefficient retrieval, and a lack of effective result verification mechanisms. To address these limitations, we propose an adaptive verifiable privacy-preserving medical image retrieval (AVPMIR) scheme in the outsourced cloud. Specifically, we utilize the convolutional neural network (CNN) ResNet50 model to extract the feature of each medical image within the dataset of the medical institution, aiming to enhance retrieval accuracy. To enhance retrieval efficiency, we build an encryption searchable index based on a mini-batch$k$-means clustering algorithm. Furthermore, we present an index merging method in which multi-data owners build a different index tree according to different standards. To check the correctness of the returned results from the cloud server, we construct an adaptive verification framework for the obtained results based on chameleon hash and BLS signature. To provide strong security for the medical image datasets, we design an improved logistic chaotic mapping algorithm. The security analysis demonstrates that AVPMIR can defend various threat models. The experiment analysis further indicates that the AVPMIR can improve retrieval efficiency and demonstrate its practicability.

Recently, to resist privacy leakage and aggregation result forgery in federated learning (FL), Wang et al. proposed a verifiable and oblivious secure aggregation protocol for FL, called VOSA. They claimed that VOSA was aggregate unforgeable and verifiable under a malicious aggregation server and gave detailed security proof. In this article, we show that VOSA is insecure, in which local gradients/aggregation results and their corresponding authentication tags/proofs can be tampered with without being detected by the verifiers. After presenting specific attacks, we analyze the reason for this security issue and give a suggestion to prevent it.

Fine-grained access control systems facilitate granting differential access rights to a set of users and allow flexibility in specifying the access rights of individual users. As an important fine-grained access control technique, key-policy attribute-based encryption (KP-ABE) has been introduced to achieve fine-grained access control over encrypted data, where each ciphertext is associated with an attribute set such that users satisfying the attribute set can decrypt the ciphertext. In the real-world application scenarios of KP-ABE, various situations such as users leaving the system, compromise of users’ private keys, and business requirements frequently occur, necessitating the revocation of decryption rights for large-scale users. To address the user revocation, numerous revocable KP-ABE schemes have been proposed. However, existing revocable KP-ABE schemes are vulnerable to quantum computer attacks. More importantly, existing solutions fail to address the user addition, where users capable of decrypting certain ciphertexts would like to grant decryption rights to others; this is a highly common requirement, such as changes in user decryption permissions and business needs. This paper explores a potentially new avenue of research to address the above issues by introducing a novel cryptographic primitive called key-policy ABE with switchable attributes (KP-ABE-SA). In the KP-ABE-SA system, each ciphertext linked to an attribute set can be transformed into one associated with another (distinct) attribute set, enabling both user revocation and addition. Furthermore, to withstand quantum computer attacks, we construct a KP-ABE-SA scheme based on the Learning with Errors (LWE) assumption, which is widely believed to be quantum-resistant. Finally, we conduct a comprehensive performance evaluation of our LWE-based KP-ABE-SA scheme, and the experimental results show that the proposed LWE-based KP-ABE-SA scheme is efficient and practical.

Zhang et al. (2023) recently proposed a secure federated learning (FL) scheme named LSFL to guarantee Byzantine robustness while protecting privacy in FL. In this work, we show that LSFL breaches privacy it claimed. Specifically, we demonstrate that the secure Byzantine robustness procedure of LSFL exposes significant information of all participant models and data to a semi-honest server, thereby damaging privacy. Then, we analyze the reason for this security issue and give a suggestion to prevent privacy breaches in LSFL.

With the widespread use of cloud storage technology by individuals and organizations, data providers usually send their data to cloud for storage to reduce memory pressure, and allow the users to retrieve these data, which has become the trend of rapid data retrieval. To guarantee the data confidentiality, several research works have been developed on encrypted cloud data for ranked multi-keyword retrieval. Nevertheless, most of these schemes are disabled since they cannot resist keyword guessing attacks. Moreover, the ranked top-$K$search results obtained by the subscriber from the encrypted cloud data are inaccurate. To overcome these drawbacks, we design a novel and efficient privacy-preserving ranked multi-keyword retrieval scheme (named as PRMKR) in this paper. With PRMKR, the data and the inverted indexes which belong to the data provider can be securely transferred to the cloud server. In addition, a registered subscriber can request accurate retrieval services without compromising his/her trapdoor information to the cloud server. Specifically, we design an encryption searchable plugin-in server and lower dimensional inverted indexesvector for data owners, which can further guarantee data confidentiality of the data owner and improve search efficiency, respectively. Our rigorous security proof demonstrates that PRMKR can withstand keyword guessing attacks. Finally, experimental evaluations confirm that PRMKR has decent computational and communication efficiency.

With the development of wireless communications and the pervasiveness of location-aware mobile electronic devices, location-based services (LBS) which can provide a convenient lifestyle for people have attracted considerable interest recently. However, there still exists the privacy disclosure problem of LBS today. To solve this problem, in this article, we present a novel privacy-preserving LBS search scheme in outsourced cloud. In the proposed LBS search scheme, the LBS providers data are first outsourced to the cloud server in an encrypted method. Then, a registered user constructs a query model to obtain accurate LBS query results without divulging his/her location information and query attribute to the LBS provider and the cloud server. Specifically, based on the designed matrix encryption technology, the LBS search scheme can achieve privacy preservation of users query and confidentiality of LBS data in the outsourced cloud server. Through security analysis, we show that our scheme can resist various known security threats. The experimental results further show that our LBS search scheme greatly reduces the communication overhead and provides convenient search experience to the users.

Data deduplication can efficiently eliminate data redundancies in cloud storage and reduce the bandwidth requirement of users. However, most previous schemes depending on the help of a trusted key server (KS) are vulnerable and limited because they suffer from revealing information, poor resistance to attacks, great computational overhead, etc. In particular, if the trusted KS fails, the whole system stops working, i.e., single-point-of-failure. In this article, we propose aSecure andEfficient dataDeduplication scheme (named SED) in a JointCloud storage system which provides the global services via collaboration with various clouds. SED also supports dynamic data update and sharing without the help of the trusted KS. Moreover, SED can overcome the single-point-of-failure that commonly occurs in the classic cloud storage system. According to the theoretical analyses, our SED ensures the semantic security in the random oracle model and has strong anti-attack ability such as the brute-force attack resistance and the collusion attack resistance. Besides, SED can effectively eliminate data redundancies with low computational complexity and communication and storage overhead. The efficiency and functionality of SED improves the usability in client-side. Finally, the comparing results show that the performance of our scheme is superior to that of the existing schemes

Compared with the classical structure with only one controller in software-defined networking (SDN), multi-controller topology structure in SDN provides a new type of cross-domain forwarding network architecture with multiple centralized controllers and distributed forwarding devices. However, when the network includes multiple domains, lack of trust among the controllers remains a challenge how to verify the correctness of cross-domain forwarding behaviors in different domains. In this paper, we propose a novel secure multi-controller rule enforcement verification (BlockREV) mechanism in SDN to guarantee the correctness of cross-domain forwarding. We first adopt blockchain technology to provide the immutability and privacy protection for forwarding behaviors. Furthermore, we present an address-based aggregate signature scheme with appropriate cryptographic primitives, which is provably secure in the random oracle model. Moreover, we design a verification algorithm based on hash values of forwarding paths to check the consistency of forwarding order. Finally, experimental results demonstrate that the proposed BlockREV mechanism is effective and suitable for multi-controller scenarios in SDN.

Frequent itemsets mining and association rules mining are among the top used algorithms in the area of data mining. Secure outsourcing of data mining tasks to the third-party cloud is an effective option for data owners. However, due to the untrust cloud and the distrust between data owners, the traditional algorithms which only work over plaintext should be re-considered to take security and privacy concerns into account. For example, each data owner may not be willing to disclose their own private data to others during the cooperative data mining process. The previous solutions are either not sufficiently secure or not efficient. Therefore, we propose aSecure andEfficientDataMiningOutsourcing (SecEDMO) scheme for secure outsourcing of frequent itemsets mining and association rules mining over the joint database (i.e., database aggregated from multiple data owners) in the paradigm of cloud computing. Based on our customized lightweight symmetric homomorphic encryption algorithm and a secure comparison algorithm, SecEDMO can ensure strong privacy protection and low data mining latency simultaneously. Moreover, the well-designed virtual transaction insertion algorithm can hide the information of the original database while still preserving the cloud’s ability to perform data mining over the obfuscated data. By evaluation of a numerical experiment and theoretical comparisons, the correctness, security, and efficiency of SecEDMO are confirmed.

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this article, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.