Privacy-enhancing aggregation of Internet of Things data via sensors grouping

Abstract

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 and regulating smart city infrastructures in a more efficient and sustainable way. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus and challenge tackled in 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 to a baseline scenario in which each individual shares 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 and its generic applicability are 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.

Optimization of privacy-utility trade-offs under informational self-determination

“…Furthermore, in most cases they rely on communication protocols that burden the system with extra computational and communication costs [38]. These costs are often prohibitive for devices such as IoT sensors and smartphone wearables in which computational power and storage are limited [3].…”

(So) Big Data and the transformation of the city

“…The proposed approach is validated using different data formats underlying many services, defined on real mobility data. From a social perspective [18], citizens' grouping can be used as a masking mechanism to lower the information revealed to third-party service providers. Grouping can be performed according to semantic criteria (for instance, the proximity of privacy preferences) or according to physical criteria (e.g., geographic location).…”

“…Grouping can be performed according to semantic criteria (for instance, the proximity of privacy preferences) or according to physical criteria (e.g., geographic location). It is shown that when data Average local group error (privacy) and global error (quality of service-aggregation accuracy) in crowdsensing via citizens' grouping [18]. Increasing the group sizes improves privacy, while quality of service remains constant Fig.…”

“…For instance, utility companies can perform energy disaggregation to infer with high accuracy the lifestyle and residential activities of citizens [55]. Similarly, the vehicle speed and locations can reveal infractions of the traffic laws and sensitive mobility patterns [18]. Moreover, centralized computations are not scalable and can be single points of failure.…”

“…Developing methods for combining different data modalities is an important research challenge. One should note again the conflict between aggregating more data sources and personal pri- Algorithmic tools addressing critical challenges in urban data science: (i) how to model information extracted from location-based social networks, (ii) TOSCA, RAMA-location detection, (iii) DITRAS-simulation of realistic mobility, (iv) MyWay-individual movement prediction [10,34,43,51,52,79,100,107,112] Visual analytics for urban data Visual analytics for geolocated social media data: photograph sharing and micro-blogging platforms [3][4][5]5,6,38,62] Shaping urban landscape Use of big data analytics for (i) recommendation to tourists (TRIPBUILDER), (ii) improving shared mobility, (iii) studying the link between human mobility, socioeconomic development, urban sustainability, and net negative cities [17,[21][22][23]27,39,44,45,60,70,101,114] SoBigData software suites Fully fledged platforms: (i) the M-Atlas tool for mining spatiotemporal data, (ii) EPOS for self-regulating sharing economies [57,95,96] Privacy-aware data gathering and protection New deal on data: (i) managing mobility data (ii) anonymization, (iii) PRUDEnce framework, (iv) DIAS [11,18,37,39,47,59,86,…”