ABSTRACT
Efficiency and privacy are the key aspects in content extraction signatures. In this study, we proposed a Secure and Efficient and Certificateless Content Extraction Signature with Privacy Protection (SECCESPP) in which scalar multiplication of elliptic curves is used to replace inefficient bilinear pairing of certificateless public key cryptosystem, and the signcryption idea is borrowed to implement privacy protection for signed messages. The correctness of the SECCESPP scheme is demonstrated by the consistency of the message and the accuracy of the equation. The security and privacy of the SECCESPP scheme are demonstrated based on the elliptic curve discrete logarithm problem in the random oracle model and are formally analyzed with the formal analysis tool ProVerif, respectively. Theory and experimental analysis show that the SECCESPP scheme is more efficient than other schemes.
Subject(s)
Algorithms , Computer Security , Privacy , Models, Theoretical , Time FactorsABSTRACT
In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.