DiP-SVM : Distribution Preserving Kernel Support Vector Machine for Big Data
Abstract-In the first place, affording secure and efficient big data aggregation methods is very attractive in the field of wireless sensor networks (WSNs) research. In real settings, the WSNs have been broadly applied, such as target tracking and environment remote monitoring. However, data can be easily compromised by a vast of attacks, such as data interception and data tampering, etc.In addition, here mainly focus on data integrity protection, give an identity-based aggregate signature (IBAS) scheme with a designated verifier for WSNs. According to the advantage of aggregate signatures, our scheme not only can keep data integrity, but also can reduce bandwidth and storage cost for WSNs. Furthermore,the security of our IBAS scheme is rigorously presented based onthe computational Diffie–Hellman assumption in random oracle model.