In recent years, the research on ciphertext-policy attribute-based encryption (CP-ABE) scheme design based on learning with errors (LWE) has been a challenging problem, and many researchers have made a lot of attempts at it. At EUROCRYPT 2021, Datta, Komargodski, and Waters proposed the first provably secure direct CP-ABE construction that supports NC${}^1$ circuit access structures. Improving on their work, we propose a CP-ABE scheme. Technically, we use the new lattice two-stage sampling technique of Lai, Liu, and Wang (EUROCRYPT 2021) in the key generation phase instead of the extended trapdoor sampling technique. In this way, we obtain a CP-ABE scheme, which is at least as secure as the original scheme under the same conditions, and has a shorter ciphertext; we provide an innovative design idea for designing CP-ABE schemes only based on LWE, although this is only a partial theoretical work.

The widespread adoption of consumer electronics has enabled patients to easily schedule appointments with doctors through online platforms, advancing the development of telemedicine. Utilizing AI technology, these platforms assist physicians in symptom analysis and providing precise recommendations, overcoming geographical barriers and achieving a balanced utilization of medical resources. Moreover, the advancement of 5G and the anticipated 6G networks has provided the capability for swift and accurate execution of cloud-based AI applications. However, online medical image consultations based on plaintext carry the risk of significant privacy breaches. By leveraging secret sharing technology, privacy leakage issues can be effectively addressed. However, the application of this technology alters the visual representation of image data. In multi-party scenarios, each party possesses only one secret share, which makes verifying the authenticity of the image challenging. To address this issue, we propose the Secure Federated Image Authentication system, which enables the authentication of encrypted images provided by consultants before the provision of services. This system employs technologies such as random feature signatures, secret sharing, and distributed reversible data hiding to achieve federated authentication of images. Through security analysis and performance evaluation, our method has demonstrated satisfactory results in terms of security and availability.

Data is typically encrypted before transmission to a cloud server to safeguard user privacy. However, such encryption prevents the server from performing searches on the data, necessitating the introduction of novel cryptographic primitives, such as public key encryption with multi-ciphertext equality test (PKEMET). PKEMET allows the cloud server to execute a single equality test on multiple ciphertexts, determining if the underlying messages are identical disclosing any extra information. Ciphertext-policy attribute-based encryption (CP-ABE) facilitates secure and efficient data sharing in cloud computing environments by supporting fine-grained access control and flexible encryption. In this work, we integrate the concepts of PKEMET and CP-ABE, introducing a novel cryptographic primitive of CP-ABE with multi-ciphertext equality test (CP-ABEMET). We present the first quantum-resistant CP-ABEMET scheme based on the learning with errors (LWE) problem. This scheme enables the receiver to delegate the cloud server to perform a single equality test among multiple messages encrypted under distinct access policies. Importantly, during the multi-ciphertexts equality test, the server gains no knowledge of the encrypted messages. Furthermore, our proposed scheme not only achieves traditional indistinguishability against chosen plaintext attack (IND-CPA) and one-way against chosen plaintext attack (OW-CPA) security but is also proven to be number secure and resilient against collusion attacks. Finally, a comparative performance analysis between our CP-ABEMET and existing equality test schemes demonstrates that our approach is more practical, efficient, and secure.

The idea of multi-keyword ranked search over encrypted cloud data has attracted considerable attention in recent studies, as it allows users to securely and efficiently retrieve highly relevant results. Traditional methods improve search efficiency by incorporating the K-means clustering algorithm. However, when applied to large-scale datasets, K-means can become computationally expensive. This paper introduces a multi-keyword ranked search method, SAO-KRS, which leverages the snow ablation optimizer (SAO) to enhance clustering performance. The approach begins with principal component analysis (PCA) to reduce the dimensionality of high-dimensional data, followed by clustering the reduced data using SAO, which reduces clustering overhead massively. By incorporating a heuristic best-first search algorithm over index trees, the scheme achieves reduced computational cost with high retrieval accuracy. In the best-case scenario, the proposed method achieves up to 21 times faster clustering and 2.7 times faster searching compared to the traditional K-means approach. Extensive experimental results verify that this method significantly improves clustering efficiency while ensuring both search speed and accuracy.