Notice of Violation of IEEE Publication Principles: Data Trading with Competitive Social Platforms: Outcomes are Mostly Privacy Welfare Damaging

Abstract

Notice of Violation of IEEE Publication Principles<br><br> “Data Trading with Competitive Social Platforms: Outcomes are Mostly Privacy Welfare Damaging” <br> by Ranjan Pal, Yixuan Wang, Junhui Li, Mingyan Liu, Jon Crowcroft, Yong Li, Sasu Tarkoma in the IEEE Transactions on Network and Service Management, Early Access, December 2020<br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br> This paper is a duplication of the original text from the paper cited below. The original text was copied with insufficient attribution and without permission.<br><br> Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br> "Too Much Data: Prices and Inefficiencies in Data Markets"<br> by Daron Acemoglu, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar NBER Working Paper No. 26296, September 2019<br><br> <br/> This paper argues that private but correlated data of individuals (such as those on a social networking platform) are under-priced in general, and that market competition for such data (such as that traded in a duopoly setting) does not alleviate this issue, resulting in diminishing economic utilitarian welfare. This is in stark contrast to the commonly held intuition that increased amount of end-user data in a market improves its efficiency or social welfare. The main reason behind this inefficiency lies in negative externality, in the form of privacy loss to one user as a result of the disclosure of another user’s data, caused by the statistical correlation between their data, which is very common among platform users. This externality is not sufficiently internalized at market equilibrium as reflected by the under-pricing of the data, thereby leading to damaged social welfare. Our mathematical model (a) provides regulatory insights on user information management for social networking platforms, and (b) paves the way for a future general theory of community data trading in n-platform oligopoly markets. To the best of our knowledge, this is the first work of its kind in relation to assessing via theory the economically inefficient nature of community data trading ventures.