Lightweight Privacy-Preserving Raw Data Publishing Scheme

Data publishing or data sharing is an important part of analyzing network environments and improving the Quality Service (QoS) in Internet Things (IoT). In order to stimulate providers (i.e., IoT end-users) contribute their data, privacy requirement necessary when collected published. traditional preservation techniques, such as k-anonymity, aggregation differential privacy, modified, aggregated, added noise, utility published are reduced. Privacy-preserving raw a more valuable solution, $n$n-source anonymity based collection most promising by delinking sources. this article, lightweight scheme for proposed, which rawness unlinkability all really guaranteed with Shamir’s secret sharing, shuffling algorithm. Moreover, it practical environment performance evaluation.