Supply-demand in information sharing
I have recently published a new paper that envisions information sharing as a self-regulatory supply-demand system. Citizens are (data) suppliers and third parties requiring access to citizens’ data are (data) consumers. The proposed system regulates the trade-off between privacy of citizens when they share data and accuracy in analytics using citizens’ shared data. Summarization of data regulates both privacy of citizens and accuracy in analytics.
The proposed work quantifies this trade-off and outlines the equilibrium dynamics using incentives and rewards. Empirical findings using real-world data from energy smart meters and the nervousnet social sensing platform confirm the cost-effectiveness and feasibility of the proposed system.
Based on this work, we are currently running the Nervousnet Social Sensing Hackathon in which participants will have the opportunity to develop and compete with alternative summarization functions to the one proposed in the published paper. Feel free to register here, you are welcome to participate from everywhere is the world. We will provide you tutorials, code utilities and all APIs to make your participation easy, fun and constructive.
Self-regulatory information sharing in participatory social sensing
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.