KEYWORDS: Data privacy, Quantum data, Machine learning, Data modeling, Computer security, Data communications, Clouds, Data processing, Data analysis, Quantum security
Cloud-assisted machine learning and analysis rely on cloud computing for various data processing services. Usually, data providers protect their data’s privacy using encryption before outsourcing, but the resulting encrypted data are known to be of poor utility. To this end, existing works combine differential privacy with public key cryptography to support multiple user analyses and queries of outsourced data. However, with the emergence of quantum computing, the schemes mentioned above based on the traditional hardness assumption will be threatened. This paper proposes quantum-secure outsourcing of privacy-preserving data publishing schemes. In particular, we develop a highly efficient proxy re-encryption scheme based on the ring learning with errors problem. This scheme allows the cloud service providers to return only the encrypted data that satisfies the user’s query without decrypting it, and the encrypted results can be decrypted using the user’s key. Meanwhile, a novel noise addition mechanism is integrated into the proxy re-encryption scheme, enabling cloud service providers to achieve a dataset sanitization procedure that supports the requests from multiple data users and allows providers to go offline after uploading their datasets. Finally, we present a detailed theoretical analysis and report an experimental evaluation of the real dataset.
KEYWORDS: Machine learning, Feature extraction, Education and training, Data modeling, Network security, Decision making, Control systems, Neural networks, Information security, Detection and tracking algorithms
Malware in the network environment is a serious threat to the security of industrial control systems. With the gradual increase of malware variants, it brings great challenges to the detection and security protection of industrial control system malware. The existing detection methods have limitations such as low intelligence in adaptive detection and recognition. In response to this problem, this paper designs a detection application method framework by combining the use of reinforcement learning, an advanced machine learning algorithm, around the malware objects that threaten the network security of industrial control systems. In the implementation process, according to the actual needs of malware behavior detection, fully combined with intelligent features such as sequential decision-making and dynamic feedback learning of reinforcement learning, the key application modules such as feature extraction network, policy network and classification network are discussed and designed in detail. The application experiments based on the actual malware test data set verify the effectiveness of the method in this paper, which can provide an intelligent decision-making aid for general malware behavior detection.
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