Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices open up tremendous opportunities for managing smart city infrastructures. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus of this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared as opposed to sharing data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system is evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed

Trust and obfuscation principles for quality of information in emerging pervasive environments

Non peer reviewedPostprin

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

On the cyber security issues of the internet infrastructure

The Internet network has received huge attentions by the research community. At a first glance, the network optimization and scalability issues dominate the efforts of researchers and vendors. Many results have been obtained in the last decades: the Internet’s architecture is optimized to be cheap, robust and ubiquitous. In contrast, such a network has never been perfectly secure. During all its evolution, the security threats of the Internet persist as a transversal and endless topic. Nowadays, the Internet network hosts a multitude of mission critical activities. The electronic voting systems and financial services are carried out through it. Governmental institutions, financial and business organizations depend on the performance and the security of the Internet. This role confers to the Internet network a critical characterization. At the same time, the Internet network is a vector of malicious activities, like Denial of Service attacks; many reports of attacks can be found in both academic outcomes and daily news. In order to mitigate this wide range of issues, many research efforts have been carried out in the past decades; unfortunately, the complex architecture and the scale of the Internet make hard the evaluation and the adoption of such proposals. In order to improve the security of the Internet, the research community can benefit from sharing real network data. Unfortunately, privacy and security concerns inhibit the release of these data: its suffices to imagine the big amount of private information (e.g., political preferences or religious belief) it is possible to get while reading the Internet packets exchanged between users and web services. This scenario motivates my research, and represents the context of this dissertation which contributes to the analysis of the security issues of the Internet infrastructures and describes relevant security proposals. In particular, the main outcomes described in this dissertation are: • the definition of a secure routing protocol for the Internet network able to provide cryptographic guarantees against false route announcement and invalid path attack; • the definition of a new obfuscation technique that allow the research community to publicly release their real network flows with formal guarantees of security and privacy; • the evidence of a new kind of leakage of sensitive informations obtained hacking the models used by sundry Machine Learning Algorithms