Guarantees of Differential Privacy in Cloud of Things: A Multilevel Data Publication Scheme

Abstract

Nowadays, the cloud computing technology combined with the new generation networks and internet of things facilitate the networking of numerous smart devices. Moreover, the advent of the smart web requires massive data backup from the smart connected devices to the cloud. Unfortunately, the publication of several of these data, such as medical information and financial transactions, could lead to serious privacy breaches, which is becoming the most serious issue in cloud of things. For instance, passive attacks can launched in order to get access to private information. For this reason, several data anonymization techniques have emerged in order to keep data as confidential as possible. However, these different techniques are making the data unusable the most of time. Meanwhile, differential privacy that has been used in a number of cyber physical systems recently emerged as an efficient technique for ensuring the privacy of cloud of things stored data. In this exploratory paper, we study the guarantees of differential privacy of a multi-level anonymization scheme of data graphs. The considered scheme disturbs the structure of the graph by adding false edges, groups the vertices in distinct sets and permutes the vertices in these groups. Particularly, we demonstrated the guarantees that the anonymized data by this algorithm remain exploitable while guaranteeing the anonymity of users.