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

Privacy in Internet of Things: A Model and Protection Framework

AbstractA new form of computation is being evolved to include massive number of diverse set of conventional computing systems, sensors, devices, equipments, software and information services and apps. This new form of computing environment is known as the “Internet-of-Things” (IoT). The adoption of IoT is fast and the “things” are becoming integral part of people day-to-day life as well as essential elements in the businesses everyday activities and processes. Open characteristics of IoT environments raises privacy concern as “things” are autonomous with some degree of authority to sharing their capabilities and knowledge to fulfil their individual or collective tasks. As such privacy becomes central and an inherit computational aspect of the “things”. The work presented here is based on modelling IoT as Cooperative Distributed Systems (CDS). It proposes a novel approach of analysing and modelling privacy concepts and concerns. Privacy protection is captured as a form of “sensitive information” management at the interaction level. A privacy protection management framework for CDS at the interaction level is proposed. The application of the framework has been demonstrated by extending Contract Net Protocol (CNP) to support privacy protection for CDS

Obfuscation and anonymization methods for locational privacy protection : a systematic literature review

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe mobile technology development combined with the business model of a majority of application companies is posing a potential risk to individuals’ privacy. Because the industry default practice is unrestricted data collection. Although, the data collection has virtuous usage in improve services and procedures; it also undermines user’s privacy. For that reason is crucial to learn what is the privacy protection mechanism state-of-art. Privacy protection can be pursued by passing new regulation and developing preserving mechanism. Understanding in what extent the current technology is capable to protect devices or systems is important to drive the advancements in the privacy preserving field, addressing the limits and challenges to deploy mechanism with a reasonable quality of Service-QoS level. This research aims to display and discuss the current privacy preserving schemes, its capabilities, limitations and challenges