Engineering Democratization in Internet of Things Data Analytics

Pournaras, Evangelos; Nikolic, Jovan; Omerzel, Alex; Helbing, Dirk

doi:10.1109/aina.2017.15

Cited by 26 publications

(54 citation statements)

References 28 publications

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“…Several important aspects should be carefully considered when using SSP for research on decisionmaking. First, we acknowledge that algorithms may be biased as well (Hajian et al 2016;Pournaras et al 2017). This is mainly because algorithms collect and process data and use tags that are created in subjective ways by humans with certain perspectives on matters.…”

Section: Concluding Remarks and Discussionmentioning

confidence: 99%

The promise of social signal processing for research on decision-making in entrepreneurial contexts

et al. 2019

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In this conceptual paper, we demonstrate how modern data science techniques can advance our understanding of important decisions in the context of entrepreneurship that involve social interactions. We know that individuals' decision-making is strongly affected by nonverbal behavior. The emerging domain of social signal processing aims at accurate computerized analysis of such behavior. Behavioral cues stemming from, for example, gestures, posture, facial expressions, and vocal expressions can now be detected and analyzed by state-of-the-art technologies utilizing artificial intelligence. This paper discusses and illustrates their potential value for future research on decision-making by entrepreneurs as well as by others yet directly affecting them (e.g., investors). In brief, social signal processing is more accurate and more efficient than conventional research methods and may reveal important characteristics that so far have been omitted in explaining decisions that are vital for firm survival and growth. We derive a total of five propositions from our newly developed conceptual framework, which we hope will be subject to extensive empirical scrutiny in future research.

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Section: Concluding Remarks and Discussionmentioning

confidence: 99%

The promise of social signal processing for research on decision-making in entrepreneurial contexts

et al. 2019

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“…It requires two types of agents representing the data consumers and data producers. This scheme can be applied in both centralized and decentralized aggregation services, such as MySQL or DIAS [36]. Finally in both heterogeneous and homogeneous systems, the data consumer can influence the data producer's choice by offering a higher amount of reward to achieve a higher utility.…”

Section: Frameworkmentioning

confidence: 99%

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

Asikis

Pournaras

2020

Future Generation Computer Systems

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The pervasiveness of Internet of Things results in vast volumes of personal data generated by smart devices of users (data producers) such as smart phones, wearables and other embedded sensors. It is a common requirement, especially for Big Data analytics systems, to transfer these large in scale and distributed data to centralized computational systems for analysis. Nevertheless, third parties that run and manage these systems (data consumers) do not always guarantee users' privacy. Their primary interest is to improve utility that is usually a metric related to the performance, costs and the quality of service. There are several techniques that mask user-generated data to ensure privacy, e.g. differential privacy. Setting up a process for masking data, referred to in this paper as a 'privacy setting', decreases on the one hand the utility of data analytics, while, on the other hand, increases privacy. This paper studies parameterizations of privacy settings that regulate the trade-off between maximum utility, minimum privacy and minimum utility, maximum privacy, where utility refers to the accuracy in the estimations of aggregation functions. Privacy settings can be universally applied as system-wide parameterizations and policies (homogeneous data sharing). Nonetheless they can also be applied autonomously by each user or decided under the influence of (monetary) incentives (heterogeneous data sharing). This latter diversity in data sharing by informational self-determination plays a key role on the privacy-utility trajectories as shown in this paper both theoretically and empirically. A generic and novel computational framework is introduced for measuring privacy-utility trade-offs and their Pareto optimization. The framework computes a broad spectrum of such trade-offs that form privacy-utility trajectories under homogeneous and heterogeneous data sharing. The practical use of the framework is experimentally evaluated using real-world data from a Smart Grid pilot project in which energy consumers protect their privacy by regulating the quality of the shared power demand data, while utility companies make accurate estimations of the aggregate load in the network to manage the power grid. Over 20, 000 differential privacy settings are applied to shape the computational trajectories that in turn provide a vast potential for data consumers and producers to participate in viable participatory data sharing systems.

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“…This setting corresponds to a centralized organization ( Figure 1), in which the aggregation is performed at a central level. The equivalent of distributed aggregatio [37,59] is a setting where data suppliers are (self-)organized [36] in groups within which they perform local aggregation in a privacy-preserving way [72,50] (Figure 2a).…”

Section: Groupingmentioning

confidence: 99%

“…The sum, together with the mean, is part of a large class of operations that can be computed in a distributed way, therefore the results are applicable in the context of the decentralized group management and aggregation shown in this paper [59,37].…”

Section: Aggregation Functionmentioning

confidence: 99%

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

Bennati

Pournaras

2018

Sustainable Cities and Society

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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.

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Engineering Democratization in Internet of Things Data Analytics

Cited by 26 publications

References 28 publications

The promise of social signal processing for research on decision-making in entrepreneurial contexts

The promise of social signal processing for research on decision-making in entrepreneurial contexts

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

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

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