The Inderscience Research Picks this week will focus on how online resources are helping people cope in different ways with the ongoing Covid-19 pandemic. Each day, we will highlight and discuss a paper from the publication the International Journal of Web-based Communities (Issue 1, volume 17, 2021)
This week, we have discussed working, education, and socialising in the online realm during the Covid-19 pandemic. This fourth paper applies more broadly than simply during the pandemic and discusses the issues of privacy in the context of online communities.
Chun Guan, Jun Hu, Yu Zhou, and Alexander Shatalov of Nanchang University in Jiangxi, China, have focused on how one’s location privacy might be preserved in this era of web-based communities and big data. The team proposes the addition of noise – spurious location data, for instance – to one’s personal “data-print” to preclude a third party, or indeed, a second party such as a service provider from, defining your path and position with any precision.
Privacy is not simply a matter for those deemed to have “something to hide”. Everyone would prefer to have control over information about themselves after all personal and private data might be exploited for nefarious purposes by others whether that is in terms of identity theft and fraud, targeted advertising, insurance premium weighting, or control by the authorities.
Mobile telecommunications devices are useful to us in many ways not least because they have sensors and software that allow the precise position of the gadget to be gleaned by various methods whether cellphone or Wi-Fi network access point or through the Global Positioning System (GPS), and perhaps other tracking technology. This location awareness allows users to benefit from a wide range of other technologies and use their device’s software in many ways that would not be possible without it. Unfortunately, the flipside to these benefits is that service providers sometimes need access to one’s location and this can be exploited by them as well as third parties. The team compares to approaches to the addition of noise in their approach and demonstrates that a “centroid” approach is the more effective.
Guan, C., Hu, J., Zhou, Y. and Shatalov, A. (2021) ‘Method of differential privacy protection for web-based communities based on adding noise to the centroid of positions’, Int. J. Web-Based Communities, Vol. 17, No. 1, pp.53–64.