Self-regulatory Information Sharing in Participatory Social Sensing

Pournaras, Evangelos, Nikolic, Jovan, Velasquez, Pablo, Trovati, Marcello, Bessis, Nik and Helbing, Dirk (2016) Self-regulatory Information Sharing in Participatory Social Sensing. EPJ Data Science, 5 (14). pp. 1-24. ISSN 2193-1127 DOI

__c1staffhome1_STAFFHOME1_Bessisn_Desktop_EPJDS_published.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview


Participation in social sensing applications is challenged by privacy threats. Large scale access to citizens' data allow surveillance and discriminatory actions that may result in segregation phenomena in society. On the contrary are the benefits of accurate computing analytics required for more informed decision-making, more effective policies and regulation of techno-socio-economic systems supported by `Internet-of Things' technologies. In contrast to earlier work that either focuses on privacy protection or Big Data analytics, this paper proposes a self-regulatory information sharing system that bridges this gap. This is achieved by modeling information sharing as a supply-demand system run by computational markets. On the supply side lie the citizens that make incentivized but self-determined decisions about the level of information they share. On the demand side stand data aggregators that provide rewards to citizens to receive the required data for accurate analytics. The system is empirically evaluated with two real-world datasets from two application domains: (i) Smart Grids and (ii) mobile phone sensing. Experimental results quantify trade-offs between privacy-preservation, accuracy of analytics and costs from the provided rewards under different experimental settings. Findings show a higher privacy-preservation that depends on the number of participating citizens and the type of data summarized. Moreover, analytics with summarization data tolerate high local errors without a significant influence on the global accuracy. In other words, local errors cancel out. Rewards can be optimized to be fair so that citizens with more significant sharing of information receive higher rewards. All these findings motivate a new paradigm of truly decentralized and ethical data analytics.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computing and Information Systems
Related URLs:
Date Deposited: 01 Apr 2016 11:00

Archive staff only

Item control page Item control page