Preference-Based Privacy Markets

Abstract

In the modern era of the mobile apps (the era of surveillance capitalism-as termed by Shoshana Zuboff) huge quantities of surveillance data about consumers and their activities offer a wave of opportunities for economic and societal value creation. ln-app advertising-a multi-billion dollar industry, is an essential part of the current digital ecosystem driven by free mobile applications, where the ecosystem entities usually comprise consumer apps, their clients (consumers), ad-networks, and advertisers. Sensitive consumer information is often being sold downstream in this ecosystem without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in longterm benefits for the consumers, it can result in serious information privacy breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this paper is: Is it economically feasible to trade consumer personal information with their formal consent (permission) and in return provide them incentives (monetary or otherwise)?. In view of (a) the behavioral assumption that humans are 'compromising' beings and have privacy preferences, (b) privacy as a good not having strict boundaries, and (c) the practical inevitability of inappropriate data leakage by data holders downstream in the data-release supply-chain, we propose a design of regulated efficient/bounded inefficient economic mechanisms for oligopoly data trading markets using a novel preference function bidding approach on a simplified sellers-broker market. Our methodology preserves the heterogeneous privacy preservation constraints (at a grouped consumer, i.e., app, level) upto certain compromise levels, and at the same time satisfies information demand (via the broker) of agencies (e.g., advertising organizations) that collect client data for the purpose of targeted behavioral advertising.

Corrections to “Preference-Based Privacy Markets”

“…The authors in [1] proposed a general mathematical skeleton to model the preference-based privacy trading scenario between multiple data sellers and a single buyer -information privacy being measured in composable tangible units (e.g., differential privacy). The skeleton, closely adapted on existing work in [2]- [4] on a compromiseinducing supply-function preference theory, reflects both, perfectly competitive and oligopolistic privacy trading structures, where the focus is on aggregate (over multiple consumers) data sellers who are mobile apps and data buyers that are ad-networks/retailers/cloud provider.…”

“…The skeleton, closely adapted on existing work in [2]- [4] on a compromiseinducing supply-function preference theory, reflects both, perfectly competitive and oligopolistic privacy trading structures, where the focus is on aggregate (over multiple consumers) data sellers who are mobile apps and data buyers that are ad-networks/retailers/cloud provider. However, the theory in [1] is directly applicable to two state-of-the-art, socially timely, and industry-viable application environments mentioned below-both of which target individual data sellers.…”

“…In the above article [1], we importantly missed out on generalizing the application scope to the inter-disciplinary contributions made in the article. It is essential to educate the readers on an increasing variety of novel and highly practical modern-day application families where the contributions made in [1] are equally applicable-not just the evident application pertaining to mobile-ad ecosystems, as in [1].…”

Designing Contracts for Trading Private and Heterogeneous Data Using a Biased Differentially Private Algorithm

“…Of the literature on data markets, the most relevant to the present paper are [4], [16], [17], [5], [6]. In [4], contracts are designed for a data market where data utility and privacy are considered, with the main conclusion that when the data collector requires a large amount of data, it is better to purchase from those who care the least about their privacy.…”

“…a maximum utilitarian social welfare state. Pal et al [16] also model a privacy trading ecosystem in the presence of an adnetwork and show that a maximum utilitarian social welfare state is achievable if the market is perfectly competitive. However, both [4] and [16] do not provide any algorithm to ensure privacy.…”