As a recently distributed machine learning framework, federated learning (FL) has garnered attention for its privacy protection. However, recent researches in recent years have shown malicious entities may still acquire the clients’ privacy in FL. Moreover, factors, such as the verifiability of the aggregation model and the huge computational and communication overheads, also make existing solutions less practical. To this end, we design a novel FL architecture named EHFL. First of all, EHFL protects data privacy by concealing the clients’ data using a single mask and group key encryption. Second, combined with symmetric balanced incomplete block design (SBIBD), our EHFL dramatically reduces client computational and communication overhead to approximately$(k+1)/(k^{2}+k+1)$compared to traditional FL (e.g., FedAvg). Third, EHFL designs an ingenious verification mechanism to ensure the correctness of the aggregation server’s results via the Hamiltonian graph formed by the SBIBD. Finally, sufficient theoretical analyses prove the reliability of EHFL and lots of experiments demonstrate the effectiveness of EHFL.

Attribute-based searchable encryption (ABSE) is a promising encryption mechanism for sharing outsourced encrypted data in clouds, allowing fine-grained access control over data while searching for encrypted data. However, the access policy in the most existing ABSE schemes exists in plaintext, which could expose sensitive information about legitimate data users. Moreover, such schemes delegate complex search operations to a cloud server, which can lead to data tampering and even untrusted results, and single point of failure. In this article, we propose a blockchain (BC)-based anonymous ABSE scheme for data sharing (BADS). First, attributes of the access policy are hidden, thus, providing confidentiality to the set of attributes that satisfy the access policy. Then combining ABSE with BC have features of tamper-proof, integrity verification, and nonrepudiation. In particular, information, such as secure index is stored in BC, while encrypted data is stored in a distributed system called the interplanetary file system (IPFS) to avoid single point of failure. Finally, BADS supports the matching algorithm that perform a fixed number of pairing operations before searching algorithm. We analysis security and evaluate performance to show the efficiency and practicability of BADS.

Secure data aggregation for smart grid aims to protect the privacy of individual data and guarantee the utility of big data. To protect user’s privacy in data aggregation, public-key-based homomorphic encryption and masking-value-based methods are adopted in existing works. However, public-key-based homomorphic encryption causes unaffordable computation costs for smart meters (SMs), while the masking-value-based works suffer from inefficient communication. Therefore, we propose a lightweight and communication efficient data aggregation (LCEDA) scheme for smart grid. First, LCEDA allows SMs to freely form the aggregation zone at lower communication and computation costs. Second, in order to ensure forward security of individual data, LCEDA achieves efficient update of masking value share, which greatly saves the complexity and computation costs compared with the existing schemes. Third, LCEDA supports dynamic enrollment and revocation of SMs to solve the malfunction and migration of SMs and improve the scalability of the LCEDA scheme. Finally, the security of LCEDA is analyzed, and extensive performance evaluations and experiments demonstrate that LCEDA is more efficient and practical.

With the rapid development of cloud computing and Internet of Things (IoT) technology, it is becoming increasingly popular for source-limited devices to outsource the massive IoT data to the cloud. How to protect data security and user privacy is an important challenge in the cloud-assisted IoT environment. Attribute-based keyword search (ABKS) has been regarded as a promising solution to ensure data confidentiality and fine-grained search control for cloud-assisted IoT. However, due to the fact that multiple users may have the same retrieval permission in ABKS, malicious users may sell their private keys on the Internet without fear of being caught. In addition, most of existing ABKS schemes do not protect the access policy which may contain privacy information. Towards this end, we present a privacy-preserving ABKS that simultaneously supports policy hiding, malicious user traceability, and revocation. Formal security analysis shows that our scheme can not only guarantee the confidentiality of keywords and access policies but also realize the traceability of malicious users. Furthermore, we provide another more efficient construction for public tracing.

Mobile crowdsensing systems use the extraction of valuable information from the data aggregation results of large-scale IoT devices to provide users with personalized services. Mobile crowdsensing combined with edge computing can improve service response speed, security, and reliability. However, previous research on data aggregation paid little attention to data verifiability and time sensitivity. In addition, existing edge-assisted data aggregation schemes do not support access control of large-scale devices. In this study, we propose a time-sensitive and verifiable data aggregation scheme (TSVA-CP-ABE) supporting access control for edge-assisted mobile crowdsensing. Specifically, in our scheme, we use attribute-based encryption for access control, where edge nodes can help IoT devices to calculate keys. Moreover, IoT devices can verify outsourced computing, and edge nodes can verify and filter aggregated data. Finally, the security of the proposed scheme is theoretically proved. The experimental results illustrate that our scheme outperforms traditional ones in both effectiveness and scalability under time-sensitive constraints.

Multiauthority ciphertext-policy attribute-based encryption (MA-CP-ABE) is a promising technique for secure data sharing in cloud storage. As multiple users with same attributes have same decryption privilege in MA-CP-ABE, the identity of the decryption key owner cannot be accurately traced by the exposed decryption key. This will lead to the key abuse problem, for example, the malicious users may sell their decryption keys to others. In this paper, we first present a traceable MA-CP-ABE scheme supporting fast access and malicious users’ accountability. Then, we prove that the proposed scheme is adaptively secure under the symmetric external Diffie–Hellman assumption and fully traceable under the q -Strong Diffie–Hellman assumption. Finally, we design a traceable and revocable MA-CP-ABE system for secure and efficient cloud storage from the proposed scheme. When a malicious user leaks his decryption key, our proposed system can not only confirm his identity but also revoke his decryption privilege. Extensive efficiency analysis results indicate that our system requires only constant number of pairing operations for ciphertext data access.