Paying for Privacy and the Personal Data Economy

Growing demands for privacy and increases in the quantity and variety of consumer data have engendered various business offerings to allow companies, and in some instances consumers, to capitalize on these developments. One such example is the emerging “personal data economy” (PDE) in which companies, such as Datacoup, purchase data directly from individuals. At the opposite end of the spectrum, the “pay-for-privacy” (PFP) model requires consumers to pay an additional fee to prevent their data from being collected and mined for advertising purposes. This Article conducts a simultaneous in-depth exploration of the impact of burgeoning PDE and PFP models. It identifies a typology of data-business models, and it uncovers thesimilarities and tensions between a data market controlled by established companies that have historically collected and mined consumer data for their primary benefit and one in which consumers play a central role in monetizing their own data. The Article makes three claims. First, it contends that PFP models facilitate thetransformation of privacy into a tradable product in the online setting, may worsen unequal access to privacy, and could further enable predatory and discriminatory behavior. Second, while the PDE may allow consumers to regain a semblance of control over their information by enabling them to decide when and with whom to share their data, consumers’ direct transfer or disclosure of personal data to companies for a price or personalized deals creates challenges similar to those found in the PFP context and generates additional concerns associated with innovative monetization techniques. Third, existing frameworks and proposals may not sufficiently ameliorate these concerns. The Article concludes by offering a path forward

Peer-to-Peer EnergyTrade: A Distributed Private Energy Trading Platform

Blockchain is increasingly being used as a distributed, anonymous, trustless framework for energy trading in smart grids. However, most of the existing solutions suffer from reliance on Trusted Third Parties (TTP), lack of privacy, and traffic and processing overheads. In our previous work, we have proposed a Secure Private Blockchain-based framework (SPB) for energy trading to address the aforementioned challenges. In this paper, we present a proof-on-concept implementation of SPB on the Ethereum private network to demonstrates SPB's applicability for energy trading. We benchmark SPB's performance against the relevant state-of-the-art. The implementation results demonstrate that SPB incurs lower overheads and monetary cost for end users to trade energy compared to existing solutions

How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market

A personal data market is a platform including three participants: data owners (individuals), data buyers and market maker. Data owners who provide personal data are compensated according to their privacy loss. Data buyers can submit a query and pay for the result according to their desired accuracy. Market maker coordinates between data owner and buyer. This framework has been previously studied based on differential privacy. However, the previous study assumes data owners can accept any level of privacy loss and data buyers can conduct the transaction without regard to the financial budget. In this paper, we propose a practical personal data trading framework that is able to strike a balance between money and privacy. In order to gain insights on user preferences, we first conducted an online survey on human attitude to- ward privacy and interest in personal data trading. Second, we identify the 5 key principles of personal data market, which is important for designing a reasonable trading frame- work and pricing mechanism. Third, we propose a reason- able trading framework for personal data which provides an overview of how the data is traded. Fourth, we propose a balanced pricing mechanism which computes the query price for data buyers and compensation for data owners (whose data are utilized) as a function of their privacy loss. The main goal is to ensure a fair trading for both parties. Finally, we will conduct an experiment to evaluate the output of our proposed pricing mechanism in comparison with other previously proposed mechanism

Privacy as a Public Good

Privacy is commonly studied as a private good: my personal data is mine to protect and control, and yours is yours. This conception of privacy misses an important component of the policy problem. An individual who is careless with data exposes not only extensive information about herself, but about others as well. The negative externalities imposed on nonconsenting outsiders by such carelessness can be productively studied in terms of welfare economics. If all relevant individuals maximize private benefit, and expect all other relevant individuals to do the same, neoclassical economic theory predicts that society will achieve a suboptimal level of privacy. This prediction holds even if all individuals cherish privacy with the same intensity. As the theoretical literature would have it, the struggle for privacy is destined to become a tragedy. But according to the experimental public-goods literature, there is hope. Like in real life, people in experiments cooperate in groups at rates well above those predicted by neoclassical theory. Groups can be aided in their struggle to produce public goods by institutions, such as communication, framing, or sanction. With these institutions, communities can manage public goods without heavy-handed government intervention. Legal scholarship has not fully engaged this problem in these terms. In this Article, we explain why privacy has aspects of a public good, and we draw lessons from both the theoretical and the empirical literature on public goods to inform the policy discourse on privacy

UN Global Pulse: Annual Report 2013

Through public-private partnerships, innovative analysis and the development of open-source methodologies, Global Pulse is strengthening public sector capacity to leverage digital Big Data for development and resilience. This report provides a brief overview of advances made during 2013

When Shopbots Meet Emails: Implications for Price Competition on the Internet

The Internet has dramatically reduced search costs for customers through tools such as shopbots. The conventional wisdom is that this reduction in search costs will increase price competition leading to a decline in prices and profits for online firms. In this paper, we provide an argument for why in contrast to conventional wisdom, competition may be reduced and prices may rise as consumer search costs for prices fall. Our argument has particular appeal in the context of the Internet, where email targeting and the ability to track and record customer behavior are institutional features that facilitate cost effective targeted pricing by firms. We show that such targeted pricing can serve as an effective counterweight to keep average prices high despite the downward pressure on prices due to low search costs. Surprisingly, we find that the effectiveness of targeting itself improves as search costs fall; therefore prices and profits can increase as search costs fall. The intuition for our argument is as follows: Consider a market where consumers are heterogeneous in their loyalty as well as their cost per unit time to search. In the brick and mortar world, it takes consumers a very large amount of time to search across multiple firms. Therefore few customers will search in equilibrium because the gains from search will be relatively small compared to the cost of search. In such a market, a firm will not be able to distinguish whether its customers bought from it due to their high loyalty or due to their unwillingness to search for low prices because of the high search cost. On the Internet, the amount of time to search across multiple stores is minimal (say zero). Now irrespective of their opportunity cost of time, all consumers can search because the time to search is negligible. If in spite of this, a consumer does not search in this environment, she is revealing that her loyalty to the firm that she buys from is very high. The key insight is that as search becomes easy for everyone, then lack of search indicates strong customer loyalty and thus can be used as a proxy to segment the market into loyal and price sensitive segments. Thanks to email technology, firms can selectively set differential prices to different customers, i.e. a high price to the loyal segment and a low price to the price sensitive segment, at relatively low cost. The increased competition due to price transparency caused by low search costs can thus be offset by the ability of firms to price discriminate between their loyal (price insensitive) customers and their price sensitive customers. In fact, we find that it can reduce the extent of competition among the firms and raise their profits. Most surprisingly, the positive effect of targeting on prices improves when search costs fall, because firms can learn more about the differences in customer loyalty, thus improving the effectiveness of targeted pricing. The effectiveness of targeted pricing however is moderated by the extent of opt-in by customers who give their permission for firms to contact them directly by email. Our analysis offers interesting strategic insights for managers about how to address the competitive problems associated with low search costs on the Internet: (1) It suggests that firms should invest in better technologies for personalization and targeted pricing so as to prevent the Internet from becoming a competitive minefield that destroys firm profitability. In fact we show that low search costs can facilitate better price personalization and can thus aid in improving the effectiveness of targeted pricing efforts. (2) The analysis also offers guidelines for online customer acquisition efforts. The critical issue for competitive advantage is not in increasing market share per se, but in increasing the loyalty of customers. While a larger share of very loyal customers reduces competitive intensity, surprisingly a larger share of customers who are not very loyal can be a competitive disadvantage. In order for customer acquisition to be profitable, it should be accompanied by a superior product or service that can ensure high loyalty. (3) Investing in online privacy initiatives that assures consumers that their private information will not be abused other than to offer them "deals" is worthwhile. Such assurances will encourage consumers to opt into firm mailing lists. This facilitates successful targeting which in turn ameliorates the competitive threats due to low search costs on the Internet. (4) When the overwhelming majority of customers are satisfied with online privacy, the remaining privacy conscious customers who are not willing to pay a higher price to maintain their privacy will be left out of the market. While this may be of some concern to privacy advocates, it is interesting that total consumer welfare can be higher even if some consumers are left out of the market. Our analysis captures the competitive implications of the interaction between two institutions facilitated by the Internet: Shopbots and Emails. But the research question addressed is more fundamental: What is the nature of competition in an environment with low costs for both consumer search and firm-to-consumer personalized communications? The strategic insights obtained in the paper may be beneficially applied even to offline businesses that can replicate such an environment. For example, offline firms could have websites on which they post prices allowing for easy price comparisons. They could also use tools such as frequency programs to create addressable databases that enable them to communicate with customers by direct mail and email (as many airlines and stores do).