As one of the most popular applications in recent years, cloud storage has been gradually integrated into all walks of life. In the field of communication techniques for the unmanned aerial vehicles (UAVs), UAVs use sensors to upload collected data to cloud servers during various exploration activities, but UAVs can only store and calculate valid information currently collected due to the limited storage and computing performance. In the actual exploration, UAVs need not only to upload complete data to the cloud server, but also to support the data dynamic update efficiently. Moreover, due to security requirements, the privacy of data uploaded by UAVs must be protected. However, the existing auditing schemes for dynamic data and integrity have many problems, such as high computation cost, low efficiency of dynamic update, low privacy and security. For this reason, we propose a public cloud audit scheme that supports dynamic data and privacy protection based on distributed string equality check protocol and Merkle-hash tree multi-level index structure. First of all, a third-party server (TPS) is set between the cloud service provider and users, which complete digital signature, integrity auditing, and data dynamic operations significantly in place of users to reduce the local computing cost. Users then locally upload data which has been encrypted to the TPS. Secondly, to further improve the security of the scheme, TPS implement signature for encrypted data based on the distributed string equality check protocol. By designing authorizations with time constraints, it is guaranteed that only the legitimate TPS with time constraints can operate with cloud servers. Finally, we implement dynamic data operation efficiently based on MHT multi-level index structure. The security proof and performance analysis show that our proposed scheme is safe and effective. INDEX TERMS Cloud Storage, communication techniques for UAVs, privacy-preserving, dynamic updating, intergrity auditing.

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With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

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With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.