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.

Modern cryptography attributes the security of a cryptographic system to the security of the key. How to securely distribute the key has always been a bottleneck in key management. This paper proposes a secure group key agreement scheme for multiple parties using a multiple twinning superlattice physical unclonable function (PUF) that can be synchronized. By sharing the challenge and helper data among multiple twinning superlattice PUF holders, the scheme employs a reusable fuzzy extractor to obtain the key locally. Moreover, adopting public-key encryption encrypts public data for establishing the subgroup key, which provides independent communication for the subgroup. At the same time, when the subgroup membership changes, the public key encrypts new public data to update the subgroup key, forming scalable group communication. This paper also presents a cost and formal security analysis, which shows that the proposed scheme can achieve computational security by applying the key obtained by the computationally secure reusable fuzzy extractor to the EAV-secure symmetric-key encryption, which has indistinguishable encryption in the presence of an eavesdropper. Additionally, the scheme is secure against physical attacks, man-in-the-middle attacks, and machine learning modeling attacks.