The Industrial Internet of Things (IIoT) is flourishing, which is unprecedentedly driven by the rapid development of wireless sensor networks (WSNs) with the assistance of cloud computing. The new wave of technology will give rise to new risks to cyber security, particularly the data confidentiality in cloud-assisted WSNs (CWSNs). Searchable public-key encryption is a promising method to address this problem. In theory, it allows sensors to upload public-key ciphertexts to the cloud, and the owner of these sensors can securely delegate a keyword search to the cloud and retrieve the intended data while maintaining data confidentiality. However, all existing and semantically secure searchable public-key encryption schemes have expensive costs in terms of generating ciphertexts and searching keywords. Hence, this paper proposes a lightweight searchable public-key encryption (LSPE) scheme with semantic security for CWSNs. LSPE reduces a large number of the computation-intensive operations that are adopted in previous works; thus, LSPE has search performance close to that of some practical searchable symmetric encryption schemes. In addition, LSPE saves considerable time and energy costs of sensors for generating ciphertexts. Finally, we experimentally test LSPE and compare the results with some previous works to quantitatively demonstrate the above advantages.

Dynamic searchable symmetric encryption (DSSE) has been widely recognized as a promising technique to delegate update and search queries over an outsourced database to an untrusted server while guaranteeing the privacy of data. Many efforts on DSSE have been devoted to obtaining a good tradeoff between security and performance. However, it appears that all existing DSSE works miss studying on what will happen if the DSSE client issues irrational update queries carelessly, such as duplicate update queries and delete queries to remove non-existent entries (that have been considered by many popular database system in the setting of plaintext). In this scenario, we find that (1) most prior works lose their claimed correctness or security, and (2) no single approach can achieve correctness, forward and backward security, and practical performance at the same time. To address this problem, we study for the first time the notion of robustness of DSSE. Generally, we say that a DSSE scheme is robust if it can keep the same correctness and security even in the case of misoperations. Then, we introduce a new cryptographic primitive named key-updatable pseudo-random function and apply this primitive to constructing ROSE, a robust DSSE scheme with forward and backward security. Finally, we demonstrate the efficiency of ROSE and give the experimental comparisons.

For the Industrial Internet of Things (IIoT), Public-key Encryption with Keyword Search (PEKS) is a type of applicable and promising encryption techniques to maintain data stored in clouds secure and searchable. To further improve the efficiency of searching, it is popular to introduce edge computing near the IIoT as a substitutes of a cloud. However, the straightforward collaboration of the two techniques performs negatively in latency-sensitive applications since the sluggish encryption of PEKS by IIoT devices negates the instant reaction of edges. To meet this challenge, we exploit the capability of the edge-cloud architecture and propose a lightweight-designed scheme called Edge-aided Searchable Public-key Encryption (ESPE). It allows IIoT devices to delegate their costly cryptographic operations to the nearby edge for fast computing and guarantees that all outsourced ciphertexts are semantically secure. Consequently, ESPE accelerates the ciphertext corresponding procedures on edges and saves over 70% encryption cost of an IIoT device.

With the outbreak of e-mail message leakage events, such as the Hillary Clinton's Email Controversy, privacy and security of sensitive e-mail information have become users' primary concern. Encrypted email seems to be a viable solution for providing security, but it will greatly limit their operations. Public encryption with keyword search (PEKS) scheme is a popular technology to incorporate security protection and favorable operability functions together, which can play an important role in searching over encrypted email in a cloud server. In this paper, we propose a practical PEKS scheme named as public-key multi-keyword searchable encryption with hidden structures (PMSEHS). It could enable e-mail receivers to do the multi-keyword and boolean search in the large encrypted email database as fast as possible, without revealing more information to the cloud server. We also give comparative experiments, which demonstrate that our scheme has a higher efficiency in multi-keyword search for encrypted emails.

Federated learning is an emerging technology which allows a server to train a global model with the cooperation of participants without exposing the participants’ data. In recent years, there have been many studies focusing on maintaining participant privacy against honest-but-curious servers. In 2017, Google proposed a promising solution that applies double masking and secret sharing tools to protect participants’ gradients for each round of federated learning (CCS’17). However, this solution fails to achieve post-quantum security and costs high communication overhead to distribute secret shares. To address this problem, this work designs a lattice-based multi-use secret sharing scheme to avoid distributing new secret shares to all participants in each round of federated learning while achieving post-quantum security. In other words, this new tool allows each participant to update his secret shares locally while maintaining the privacy of participants’ gradients against quantum attacks. Finally, this work applies this new secret sharing technique to construct a lattice-based federated learning protocl LaF . The theoretic analysis demonstrates that LaF saves a lot of communication costs compared with Google’s solution, and the experimental results show that LaF achieves higher runtime efficiency.

Dynamic Searchable Symmetric Encryption (DSSE) is a prospective technique in the field of cloud storage for secure search over encrypted data. A DSSE client can issue update queries to an honest-but-curious server for adding or deleting his ciphertexts to or from the server and delegate keyword search over those ciphertexts to the server. Numerous investigations focus on achieving strong security, like forward-and-Type-I−-backward security, to reduce the information leakage of DSSE to the server as much as possible. However, the existing DSSE with such strong security cannot keep search correctness and stable security (or robustness, in short) if irrational queries are issued by the client, like duplicate add or delete queries and the delete queries for removing non-existed entries, to the server unintentionally. Hence, this work proposes two new DSSE schemes, named $\mathtt {SR-DSSE}_{a}$ and $\mathtt {SR-DSSE}_{b}$ , respectively. Both two schemes achieve forward-and-Type-I−-backward security while keeping robustness when irrational queries are issued. In terms of performance, $\mathtt {SR-DSSE}_{a}$ has more efficient communication costs and roundtrips than $\mathtt {SR-DSSE}_{b}$ . In contrast, $\mathtt {SR-DSSE}_{b}$ has a more efficient search performance than $\mathtt {SR-DSSE}_{a}$ . Its search performance is close to the existing DSSE scheme with the same security but fails to achieve robustness.