Self-regulatory information sharing in participatory social sensing

Evangelos Pournaras, Jovan Nikolic, Pablo Velasquez, Marcello Trovati, Nik Bessis, Dirk Helbing

Research output: Contribution to journalArticle

10 Citations (Scopus)
79 Downloads (Pure)

Abstract

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 oftechno-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 modelling 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 sides tan 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.
Original languageEnglish
Pages (from-to)1-24
JournalEPJ Data Science
Volume5
Issue number14
Early online date1 Apr 2016
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • privacy
  • summarization
  • analytics
  • aggregation
  • self-regulation
  • social sensing
  • supply-demand
  • reward
  • incentive

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