Статті в журналах: "Price of privacy"

1

Naumov, Pavel, and Jia Tao. "Price of privacy." Journal of Applied Logic 20 (March 2017): 32–48. http://dx.doi.org/10.1016/j.jal.2016.11.035.

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2

Stewart-Amidei, Chris. "The Price of Privacy." Journal of Neuroscience Nursing 35, no. 5 (October 2003): 241. http://dx.doi.org/10.1097/01376517-200310000-00001.

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3

Lesk, Michael. "The Price of Privacy." IEEE Security & Privacy 10, no. 5 (September 2012): 79–81. http://dx.doi.org/10.1109/msp.2012.133.

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4

Baumann, Annika, Johannes Haupt, Fabian Gebert, and Stefan Lessmann. "The Price of Privacy." Business & Information Systems Engineering 61, no. 4 (February 21, 2018): 413–31. http://dx.doi.org/10.1007/s12599-018-0528-2.

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5

Ichihashi, Shota. "Online Privacy and Information Disclosure by Consumers." American Economic Review 110, no. 2 (February 1, 2020): 569–95. http://dx.doi.org/10.1257/aer.20181052.

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I study the welfare and price implications of consumer privacy. A consumer discloses information to a multiproduct seller, which learns about his preferences, sets prices, and makes product recommendations. Although the consumer benefits from accurate recommendations, the seller may use the information to price discriminate. I show that the seller prefers to commit to not use information for pricing in order to encourage information disclosure. However, this commitment hurts the consumer, who could be better off by precommitting to withhold some information. In contrast to single-product models, total surplus may be lower if the seller can base prices on information. (JEL D11, D83, L81, M31)

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6

Fuller, Caleb S. "Privacy law as price control." European Journal of Law and Economics 45, no. 2 (July 26, 2017): 225–50. http://dx.doi.org/10.1007/s10657-017-9563-6.

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7

Kummer, Michael, and Patrick Schulte. "When Private Information Settles the Bill: Money and Privacy in Google’s Market for Smartphone Applications." Management Science 65, no. 8 (August 2019): 3470–94. http://dx.doi.org/10.1287/mnsc.2018.3132.

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We shed light on a money-for-privacy trade-off in the market for smartphone applications (“apps”). Developers offer their apps at lower prices in return for greater access to personal information, and consumers choose between low prices and more privacy. We provide evidence for this pattern using data from 300,000 apps obtained from the Google Play Store (formerly Android Market) in 2012 and 2014. Our findings show that the market’s supply and demand sides both consider an app’s ability to collect private information, measured by the apps’s use of privacy-sensitive permissions: (1) cheaper apps use more privacy-sensitive permissions; (2) given price and functionality, demand is lower for apps with sensitive permissions; and (3) the strength of this relationship depends on contextual factors, such as the targeted user group, the app’s previous success, and its category. Our results are robust and consistent across several robustness checks, including the use of panel data, a difference-in-differences analysis, “twin” pairs of apps, and various measures of privacy-sensitivity and app demand. This paper was accepted by Anandhi Bharadwaj, information systems.

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8

JOSHI, SUMIT, YU-AN SUN, and POORVI L. VORA. "PRICE DISCRIMINATION AND PRIVACY: A NOTE." International Game Theory Review 13, no. 01 (March 2011): 83–92. http://dx.doi.org/10.1142/s0219198911002861.

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In many instances of price discrimination, a seller of an item is in possession of signals from competing buyers regarding their private valuation for the item. If the seller uses this information to price discriminate against the buyer, buyers would correspondingly modify their signalling strategy. Our paper shows that the seller can gain by sometimes strategically ignoring the information contained in the signals and pricing the item in a non-discriminatory way. This "mixed" strategy induces buyers to send more informative signals in equilibrium than if the seller were to always price discriminate. Thus the seller can offset any revenue loss in states where he ignores information by the gains made in states where he can price discriminate more effectively due to the larger amount of information now communicated in the signals.

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9

CACM Staff. "The blood price of unrestricted privacy." Communications of the ACM 65, no. 11 (November 2022): 8–9. http://dx.doi.org/10.1145/3563965.

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10

Acquisti, Alessandro. "Identity Management, Privacy, and Price Discrimination." IEEE Security & Privacy Magazine 6, no. 2 (March 2008): 46–50. http://dx.doi.org/10.1109/msp.2008.35.

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11

Crippen, David. "Internet information processing: what price privacy." Journal of Critical Care 22, no. 1 (March 2007): 32–33. http://dx.doi.org/10.1016/j.jcrc.2006.12.010.

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12

Liu, Wen-Jie, Chun-Tang Li, Yu Zheng, Yong Xu, and Yin-Song Xu. "Quantum Privacy-Preserving Price E-Negotiation." International Journal of Theoretical Physics 58, no. 10 (August 7, 2019): 3259–70. http://dx.doi.org/10.1007/s10773-019-04201-9.

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13

Ali, S. Nageeb, Greg Lewis, and Shoshana Vasserman. "Consumer Control and Privacy Policies." AEA Papers and Proceedings 113 (May 1, 2023): 204–9. http://dx.doi.org/10.1257/pandp.20231085.

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Анотація:

Firms use consumer data to price discriminate. In response, policymakers have pushed for consumers to have control over their data so that they choose what to share and with whom. We model consumer control and its effect on markets through the lens of voluntary disclosure: the consumer has characteristics that they can verifiably disclose to the market. The market in turn draws inferences in equilibrium. Using this framework, we identify when consumers benefit from price discrimination in monopolistic and competitive markets. We also show that this framework can resolve the privacy paradox and potentially offers new limits for price discrimination.

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14

Karas, Laura. "Privacy as the Price of Drug Access." Science and Technology Law Review 23, no. 1 (March 7, 2022): 50–141. http://dx.doi.org/10.52214/stlr.v23i1.9390.

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In response to the recent increase in FDA-approved specialty drugs and escalating specialty drug prices, drug companies now offer patient support programs (“PSPs”) for eligible patients prescribed a particular pharmaceutical drug. Such programs encompass both financial assistance for the purchase of a specialty drug and behavioral services, including nursing support and injection training, intended to improve drug adherence. Although ostensibly gratuitous, these programs have a steep and underappreciated cost: disclosure of protected health information. In effect, patient support programs compel patients to trade protected health information for drug access. This Article provides the first in- depth examination of the legal and ethical concerns associated with patient support programs. Enrollment in a drug company’s patient support program furnishes the company with linked patient- and prescriber- identifying information for each enrollee, data which may enable drug companies to target marketing to patients and healthcare providers with an otherwise unattainable degree of precision. Moreover, once a drug company acquires an enrollee’s protected health information pursuant to a valid Health Insurance Portability and Accountability Act (HIPAA) authorization, a drug company faces few limits on downstream uses of those data. This Article illuminates a possible role for patient support program-mediated data collection in two unlawful drug company practices: (1) kickback schemes in coordination with foundations that cover pharmaceutical drug copays, and (2) “product hopping” to a new brand-name drug formulation after patent expiration of an older formulation. The current regime for health data privacy in the United States lacks adequate safeguards to prevent drug companies from exploiting patient support program-derived data to the detriment of patients. The Article ends by proposing practical modifications to the HIPAA Privacy Rule to modernize HIPAA’s protections vis-à-vis health data transferred from covered entities to noncovered entities such as drug companies.

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15

Loertscher, Simon, and Leslie M. Marx. "Digital monopolies: Privacy protection or price regulation?" International Journal of Industrial Organization 71 (July 2020): 102623. http://dx.doi.org/10.1016/j.ijindorg.2020.102623.

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16

Jung, Kangsoo, and Seog Park. "Data Privacy-Price Negotiation for applying Differential Privacy in Data Market Environments." Journal of KIISE 46, no. 4 (April 30, 2019): 376–84. http://dx.doi.org/10.5626/jok.2019.46.4.376.

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17

Hilmola, Olli-Pekka. "On Prices of Privacy Coins and Bitcoin." Journal of Risk and Financial Management 14, no. 8 (August 6, 2021): 361. http://dx.doi.org/10.3390/jrfm14080361.

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Since the inauguration of cryptocurrencies, Bitcoin has been under pressure from competing tokens. As Bitcoin is a public open ledger blockchain coin, it has its weaknesses in privacy and anonymity. In the recent decade numerous coins have been initiated as privacy coins, which try to tackle these weaknesses. This research compares mostly mature privacy coins to Bitcoin, and comparison is made from a price perspective. It seems that Bitcoin is leading privacy coins in price terms, and correlation is typically high and positive. From the earlier crypto market peak of 2017–18, only a very small number of coins are showing positive returns in 2021. It is typical that many privacy coins have lost substantial amounts of their value (ranging 80–90%) or that they do not exist anymore at all. Only Horizen and Monero have shown long-term sustainability in their value; however, their price changes follow that of Bitcoin very closely. The role of privacy coins in the future remains as an open issue.

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18

Lin, Xudong, Xiaoli Huang, Shuilin Liu, Yulin Li, Hanyang Luo, and Sumin Yu. "Competitive Price-Quality Strategy of Platforms under User Privacy Concerns." Journal of Theoretical and Applied Electronic Commerce Research 17, no. 2 (April 26, 2022): 571–89. http://dx.doi.org/10.3390/jtaer17020030.

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The behavior-based discrimination price model (BBPD) needs to collect a large amount of user information, which would spark user privacy concerns. However, the literature on BBPD typically overlooks consumer privacy concerns. Additionally, most of the existing research provides some insights from the perspective of traditional privacy protection measures, but seldom discusses the role of quality discrimination in alleviating users’ privacy concerns. By establishing a Hotelling duopoly model of two-period price-quality competition, this paper explores the impact of quality discrimination on industry profits, user surplus, and social welfare under user privacy concerns. The results show that, with the increase of user privacy cost, given weak market competition intensity, quality discrimination can increase users’ surplus and social welfare, thereby alleviating users’ privacy concerns. We then discuss the managerial implications for alleviating consumer privacy concerns. In addition, we take Airbnb as an example to provide practical implications.

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19

Lin, Xudong, Xiaoli Huang, Shuilin Liu, Yulin Li, Hanyang Luo, and Sumin Yu. "Competitive Price-Quality Strategy of Platforms under User Privacy Concerns." Journal of Theoretical and Applied Electronic Commerce Research 17, no. 2 (April 26, 2022): 571–89. http://dx.doi.org/10.3390/jtaer17020030.

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Анотація:

The behavior-based discrimination price model (BBPD) needs to collect a large amount of user information, which would spark user privacy concerns. However, the literature on BBPD typically overlooks consumer privacy concerns. Additionally, most of the existing research provides some insights from the perspective of traditional privacy protection measures, but seldom discusses the role of quality discrimination in alleviating users’ privacy concerns. By establishing a Hotelling duopoly model of two-period price-quality competition, this paper explores the impact of quality discrimination on industry profits, user surplus, and social welfare under user privacy concerns. The results show that, with the increase of user privacy cost, given weak market competition intensity, quality discrimination can increase users’ surplus and social welfare, thereby alleviating users’ privacy concerns. We then discuss the managerial implications for alleviating consumer privacy concerns. In addition, we take Airbnb as an example to provide practical implications.

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20

de Cornière, Alexandre, and Rodrigo Montes. "Consumer Privacy and the Incentives to Price-Discriminate in Online Markets." Review of Network Economics 16, no. 3 (September 26, 2017): 291–305. http://dx.doi.org/10.1515/rne-2018-0004.

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Abstract This paper studies how product customization and consumer privacy affect a monopolist’s incentives to engage in perfect price discrimination. We consider a monopolist that faces an ex ante choice to commit to price discrimination or to a uniform price. We introduce a simple model in which a monopolist can use analytics to access consumer data to both price-discriminate and offer customized products. In turn, consumers can protect their privacy to avoid price discrimination at a cost. By committing not to price-discriminate, the firm induces consumers to not protect their data, which allows it to customize the product. It can then extract the extra value through an increased uniform price. This strategy is profitable when the value added through customization is sufficiently high. An intermediate quality of analytics gives the monopolist more incentives to set a uniform price.

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21

Zhang, Mingwu, and Bingruolan Zhou. "PP-VCA: A Privacy-Preserving and Verifiable Combinatorial Auction Mechanism." Wireless Communications and Mobile Computing 2020 (October 19, 2020): 1–11. http://dx.doi.org/10.1155/2020/8888284.

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Combinatorial auctions can be employed in the fields such as spectrum auction, network routing, railroad segment, and energy auction, which allow multiple goods to be sold simultaneously and any combination of goods to be bid and the maximum sum of combinations of bidding prices to be calculated. However, in traditional combinatorial auction mechanisms, data concerning bidders’ price and bundle might reveal sensitive information, such as personal preference and competitive relation since the winner determination problem needs to be resolved in terms of sensitive data as above. In order to solve this issue, this paper exploits a privacy-preserving and verifiable combinatorial auction protocol (PP-VCA) to protect bidders’ privacy and ensure the correct auction price in a secure manner, in which we design a one-way and monotonically increasing function to protect a bidder’s bid to enable the auctioneer to pick out the largest bid without revealing any information about bids. Moreover, we design and employ three subprotocols, namely, privacy-preserving winner determination protocol, privacy-preserving scalar protocol, and privacy-preserving verifiable payment determination protocol, to implement the combinatorial auction with bidder privacy and payment verifiability. The results of comprehensive experimental evaluations indicate that our proposed scheme provides a better efficiency and flexibility to meet different types of data volume in terms of the number of goods and bidders.

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22

Banerjee, Siddhartha, Nidhi Hegde, and Laurent Massoulie. "The Price of Privacy in Untrusted Recommender Systems." IEEE Journal of Selected Topics in Signal Processing 9, no. 7 (October 2015): 1319–31. http://dx.doi.org/10.1109/jstsp.2015.2423254.

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23

Steinfeld, Nili. "Trading with privacy: the price of personal information." Online Information Review 39, no. 7 (November 9, 2015): 923–38. http://dx.doi.org/10.1108/oir-05-2015-0168.

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Purpose – The purpose of this paper is to examine how users in an anonymous virtual environment react to an offer to trade in access to their social network profile. Design/methodology/approach – The experiment was conducted in Second Life (SL). Participants were offered varied sums of money in exchange for access to their Facebook profile, effectively undermining their anonymity. Findings – Even in an anonymous environment, money plays a role in users’ decisions to disclose their offline identity, but a closer look at the findings reveals that users also use deception to enjoy the benefits of the offer without paying the costs. The results illustrate three types of users according to the strategies they employ: abstainers, traders, and deceivers. Research limitations/implications – The implications to the field of online information disclosure lie at the ability to illustrate and distinguish between the different strategies users choose with regard to online information disclosure, as the study design simulates a common information disclosure trade offer in online environments. Originality/value – Unlike previous studies that focussed on trades with specific pieces of information, this study examines willingness to sell access to a user’s entire profile, by thus better simulating online services conduct. This is also the first privacy experiment conducted in the anonymous environment of SL, and the first to study deception as a privacy protection strategy.

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24

Zuiderveen Borgesius, Frederik, and Joost Poort. "Online Price Discrimination and EU Data Privacy Law." Journal of Consumer Policy 40, no. 3 (July 15, 2017): 347–66. http://dx.doi.org/10.1007/s10603-017-9354-z.

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25

Carroll, Brian. "Price of privacy: Selling consumer databases in bankruptcy." Journal of Interactive Marketing 16, no. 3 (January 2002): 47–58. http://dx.doi.org/10.1002/dir.10036.

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26

Cloos, Janis, Björn Frank, Lukas Kampenhuber, Stephany Karam, Nhat Luong, Daniel Möller, Maria Monge-Larrain, Nguyen Tan Dat, Marco Nilgen, and Christoph Rössler. "Is Your Privacy for Sale? An Experiment on the Willingness to Reveal Sensitive Information." Games 10, no. 3 (July 5, 2019): 28. http://dx.doi.org/10.3390/g10030028.

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We investigate whether individuals’ self-stated privacy behavior is correlated with their reservation price for the disclosure of personal and potentially sensitive information. Our incentivized experiment has a unique setting: Information about choices with real implications could be immediately disclosed to an audience of fellow first semester students. Although we find a positive correlation between respondents’ willingness to accept (WTA) disclosure of their private information and their stated privacy behavior for some models, this correlation disappears when we change the specification of the privacy index. Independent of the privacy index chosen we find that the WTA is significantly influenced by individual responses to personal questions, as well as by different decisions to donate actual money, indicating that the willingness to protect private information depends on the delicacy of the information at stake.

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27

Tassa, Tamir, Tal Grinshpoun, and Roie Zivan. "Privacy Preserving Implementation of the Max-Sum Algorithm and its Variants." Journal of Artificial Intelligence Research 59 (July 17, 2017): 311–49. http://dx.doi.org/10.1613/jair.5504.

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One of the basic motivations for solving DCOPs is maintaining agents' privacy. Thus, researchers have evaluated the privacy loss of DCOP algorithms and defined corresponding notions of privacy preservation for secured DCOP algorithms. However, no secured protocol was proposed for Max-Sum, which is among the most studied DCOP algorithms. As part of the ongoing effort of designing secure DCOP algorithms, we propose P-Max-Sum, the first private algorithm that is based on Max-Sum. The proposed algorithm has multiple agents preforming the role of each node in the factor graph, on which the Max-Sum algorithm operates. P-Max-Sum preserves three types of privacy: topology privacy, constraint privacy, and assignment/decision privacy. By allowing a single call to a trusted coordinator, P-Max-Sum also preserves agent privacy. The two main cryptographic means that enable this privacy preservation are secret sharing and homomorphic encryption. In addition, we design privacy-preserving implementations of four variants of Max-Sum. We conclude by analyzing the price of privacy in terns of runtime overhead, both theoretically and by extensive experimentation.

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28

Pejó, Balázs, Qiang Tang, and Gergely Biczók. "Together or Alone: The Price of Privacy in Collaborative Learning." Proceedings on Privacy Enhancing Technologies 2019, no. 2 (April 1, 2019): 47–65. http://dx.doi.org/10.2478/popets-2019-0019.

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Abstract Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model. For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player. Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium.

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29

Ausubel, Lawrence M. "An Efficient Ascending-Bid Auction for Multiple Objects." American Economic Review 94, no. 5 (November 1, 2004): 1452–75. http://dx.doi.org/10.1257/0002828043052330.

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When bidders exhibit multi-unit demands, standard auction methods generally yield inefficient outcomes. This article proposes a new ascending-bid auction for homogeneous goods, such as Treasury bills or telecommunications spectrum. The auctioneer announces a price and bidders respond with quantities. Items are awarded at the current price whenever they are “clinched,” and the price is incremented until the market clears. With private values, this (dynamic) auction yields the same outcome as the (sealed-bid) Vickrey auction, but has advantages of simplicity and privacy preservation. With interdependent values, this auction may retain efficiency, whereas the Vickrey auction suffers from a generalized Winner's Curse.

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30

Sui, Xin, and Craig Boutilier. "Efficiency and Privacy Tradeoffs in Mechanism Design." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 738–44. http://dx.doi.org/10.1609/aaai.v25i1.7865.

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A key problem in mechanism design is the construction of protocols that reach socially efficient decisions with minimal information revelation. This can reduce agent communication, and further, potentially increase privacy in the sense that agents reveal no more private information than is needed to determine an optimal outcome. This is not always possible: previous work has explored the tradeoff between communication cost and efficiency, and more recently, communication and privacy. We explore a third dimension: the tradeoff between privacy and efficiency. By sacrificing efficiency, we can improve the privacy of a variety of existing mechanisms. We analyze these tradeoffs in both second-price auctions and facility location problems (introducing new incremental mechanisms for facility location along the way). Our results show that sacrifices in efficiency can provide gains in privacy (and communication), in both the average and worst case.

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31

Lu, Chenbei, Jiaman Wu, and Chenye Wu. "Privacy-preserving decentralized price coordination for EV charging stations." Electric Power Systems Research 212 (November 2022): 108355. http://dx.doi.org/10.1016/j.epsr.2022.108355.

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32

Pachilakis, Michalis, Panagiotis Papadopoulos, Nikolaos Laoutaris, Evangelos P. Markatos, and Nicolas Kourtellis. "YourAdvalue: Measuring Advertising Price Dynamics without Bankrupting User Privacy." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 3 (December 14, 2021): 1–26. http://dx.doi.org/10.1145/3491044.

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The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktop RTB prices in the wild, and compare them with desktop and mobile RTB prices reported by past work. We present how it estimates ad prices that are encrypted, and how it preserves user privacy while reporting results back to a data-server for analysis. We deployed our system, disseminated its browser extension, and collected data from 200 users, including 12000 ad impressions over 11 months. By analyzing this dataset, we show that desktop RTB prices have grown 4.6x over desktop RTB prices measured in 2013, and 3.8x over mobile RTB prices measured in 2015. We also study how user demographics associate with the intensity of RTB ecosystem tracking, leading to higher ad prices. We find that exchanging data between advertisers and/or data brokers through cookie-synchronization increases the median value of display ads by 19%. We also find that female and younger users are more targeted, suffering more tracking (via cookie synchronization) than male or elder users. As a result of this targeting in our dataset, the advertising value (i) of women is 2.4x higher than that of men, (ii) of 25-34 year-olds is 2.5x higher than that of 35-44 year-olds, (iii) is most expensive on weekends and early mornings.

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33

Bun, Mark, Jonathan Ullman, and Salil Vadhan. "Fingerprinting Codes and the Price of Approximate Differential Privacy." SIAM Journal on Computing 47, no. 5 (January 2018): 1888–938. http://dx.doi.org/10.1137/15m1033587.

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34

Sandıkçı, Burhaneddin, Lisa M. Maillart, Andrew J. Schaefer, Oguzhan Alagoz, and Mark S. Roberts. "Estimating the Patient's Price of Privacy in Liver Transplantation." Operations Research 56, no. 6 (December 2008): 1393–410. http://dx.doi.org/10.1287/opre.1080.0648.

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35

Lin, Xudong, Xiaoli Huang, Shuilin Liu, Yulin Li, Hanyang Luo, and Sumin Yu. "Social Welfare Analysis under Different Levels of Consumers’ Privacy Regulation." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 7 (October 27, 2021): 2943–64. http://dx.doi.org/10.3390/jtaer16070161.

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Анотація:

With the rapid development of information technology, digital platforms can collect, utilize, and share large amounts of specific information of consumers. However, these behaviors may endanger information security, thus causing privacy concerns among consumers. Considering the information sharing among firms, this paper constructs a two-period duopoly price competition Hotelling model, and gives insight into the impact of three different levels of privacy regulations on industry profit, consumer surplus, and social welfare. The results show that strong privacy protection does not necessarily make consumers better off, and weak privacy protection does not necessarily hurt consumers. Information sharing among firms will lead to strong competitive effects, which will prompt firms to lower the price for new customers, thus damaging the profits of firms, and making consumers’ surplus higher. The level of social welfare under different privacy regulations depends on consumers’ product-privacy preference, and the cost of information coordination among firms. With the cost of information coordination among firms increasing, it is only in areas where consumers have greater privacy preferences that social welfare may be optimal under the weak regulation.

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36

Hoffmann, Florian, Roman Inderst, and Marco Ottaviani. "Persuasion Through Selective Disclosure: Implications for Marketing, Campaigning, and Privacy Regulation." Management Science 66, no. 11 (November 2020): 4958–79. http://dx.doi.org/10.1287/mnsc.2019.3455.

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This paper models how firms or political campaigners (senders) persuade consumers and voters (receivers) by selectively disclosing information about their offering depending on individual receivers' preferences and orientations. We derive positive and normative implications depending on the extent of competition among senders, whether receivers are wary of senders collecting personalized data, and whether firms are able to personalize prices. We show how both senders and receivers can benefit from selective disclosure. Privacy laws requiring senders to obtain consent to acquire personal information that enables such selective disclosure increases receiver welfare if and only if there is little or asymmetric competition among senders, if receivers are unwary, and if firms can price discriminate. This paper has been accepted by Joshua Gans, business strategy.

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37

Moskvitch, K. "Privacy and snooping in smart cities: who pays the price?" Engineering & Technology 11, no. 1 (February 1, 2016): 40–42. http://dx.doi.org/10.1049/et.2016.0103.

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38

Mai, Bin, Nirup M. Menon, and Sumit Sarkar. "No Free Lunch: Price Premium for Privacy Seal-Bearing Vendors." Journal of Management Information Systems 27, no. 2 (October 2010): 189–212. http://dx.doi.org/10.2753/mis0742-1222270206.

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39

Belleflamme, Paul, and Wouter Vergote. "Monopoly price discrimination and privacy: The hidden cost of hiding." Economics Letters 149 (December 2016): 141–44. http://dx.doi.org/10.1016/j.econlet.2016.10.027.

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40

Zuiderveen Borgesius, Frederik, and Joost Poort. "Erratum to: Online Price Discrimination and EU Data Privacy Law." Journal of Consumer Policy 40, no. 4 (September 4, 2017): 521. http://dx.doi.org/10.1007/s10603-017-9360-1.

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41

Soodan, Vishal, and Avinash Rana. "Modeling Customers' Intention to Use E-Wallet in a Developing Nation." Journal of Electronic Commerce in Organizations 18, no. 1 (January 2020): 89–114. http://dx.doi.org/10.4018/jeco.2020010105.

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Intention to use e-wallets is affected by a number of factors which are related to consumer perception about privacy, security, price value, benefits etc. A sample of 613 customers of e-wallets in Punjab state in India was validated through mall intercept method. The results indicate that hedonic motivation, perceived security, general privacy, facilitating conditions, performance expectancy, perceived savings and social influence, and price value in this order, influence the intention to adopt e-wallets. Habit and effort expectancy are the hindrances that have a negative impact on the e-wallet adoption. Factors such as hedonic motivations, security, and privacy have larger roles. The service providers should maintain the privacy and security of users and engage customers by modifying the existing services' range and features. The study endorses reduction in the efforts of using e-wallets, and the conversion of habit into more willingly performed behavior. The resulting model can draw meaningful insights about adoption of this emerging payment platform.

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Argenziano, Rossella, and Alessandro Bonatti. "Data Markets with Privacy-Conscious Consumers." AEA Papers and Proceedings 113 (May 1, 2023): 191–96. http://dx.doi.org/10.1257/pandp.20231083.

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We study data linkages among heterogeneous firms and examine how they shape the outcome of privacy regulation. A single consumer interacts sequentially with two firms: one firm collects data on consumer behavior, and the other firm leverages the data to set a quality level and a price. Privacy-conscious consumers distort their purchases from a data-collecting firm to manipulate the data-using firm's beliefs. We identify conditions under which data linkages increase total firm profits. Therefore, if firms can trade consumer data efficiently, our setting provides a rationale for the existence of data markets even with privacy-conscious consumers.

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Han, Catherine, Irwin Reyes, Álvaro Feal, Joel Reardon, Primal Wijesekera, Narseo Vallina-Rodriguez, Amit Elazari, Kenneth A. Bamberger, and Serge Egelman. "The Price is (Not) Right: Comparing Privacy in Free and Paid Apps." Proceedings on Privacy Enhancing Technologies 2020, no. 3 (July 1, 2020): 222–42. http://dx.doi.org/10.2478/popets-2020-0050.

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AbstractIt is commonly assumed that “free” mobile apps come at the cost of consumer privacy and that paying for apps could offer consumers protection from behavioral advertising and long-term tracking. This work empirically evaluates the validity of this assumption by comparing the privacy practices of free apps and their paid premium versions, while also gauging consumer expectations surrounding free and paid apps. We use both static and dynamic analysis to examine 5,877 pairs of free Android apps and their paid counterparts for differences in data collection practices and privacy policies between pairs. To understand user expectations for paid apps, we conducted a 998-participant online survey and found that consumers expect paid apps to have better security and privacy behaviors. However, there is no clear evidence that paying for an app will actually guarantee protection from extensive data collection in practice. Given that the free version had at least one thirdparty library or dangerous permission, respectively, we discovered that 45% of the paid versions reused all of the same third-party libraries as their free versions, and 74% of the paid versions had all of the dangerous permissions held by the free app. Likewise, our dynamic analysis revealed that 32% of the paid apps exhibit all of the same data collection and transmission behaviors as their free counterparts. Finally, we found that 40% of apps did not have a privacy policy link in the Google Play Store and that only 3.7% of the pairs that did reflected differences between the free and paid versions.

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Liu, Jinfei, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, and Jimeng Sun. "Dealer." Proceedings of the VLDB Endowment 14, no. 6 (February 2021): 957–69. http://dx.doi.org/10.14778/3447689.3447700.

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Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace perspective and propose the first en D -to-end mod e l m a rketp l ace with diff e rential p r ivacy ( Dealer ) towards answering the following questions: How to formulate data owners' compensation functions and model buyers' price functions? How can the broker determine prices for a set of models to maximize the revenue with arbitrage-free guarantee, and train a set of models with maximum Shapley coverage given a manufacturing budget to remain competitive ? For the former, we propose compensation function for each data owner based on Shapley value and privacy sensitivity, and price function for each model buyer based on Shapley coverage sensitivity and noise sensitivity. Both privacy sensitivity and noise sensitivity are measured by the level of differential privacy. For the latter, we formulate two optimization problems for model pricing and model training, and propose efficient dynamic programming algorithms. Experiment results on the real chess dataset and synthetic datasets justify the design of Dealer and verify the efficiency and effectiveness of the proposed algorithms.

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Acemoglu, Daron, Ali Makhdoumi, Azarakhsh Malekian, and Asu Ozdaglar. "Too Much Data: Prices and Inefficiencies in Data Markets." American Economic Journal: Microeconomics 14, no. 4 (November 1, 2022): 218–56. http://dx.doi.org/10.1257/mic.20200200.

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When a user shares her data with online platforms, she reveals information about others. In such a setting, externalities depress the price of data because once a user's information is leaked by others, she has less reason to protect her data and privacy. These depressed prices lead to excessive data sharing. We characterize conditions under which shutting down data markets improves welfare. Platform competition does not redress the problem of excessively low data prices and too much data sharing and may further reduce welfare. We propose a scheme based on mediated data sharing that improves efficiency. (JEL D62, D83, H23, L51, L86, L88)

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Yang, Jian, and Chunxiao Xing. "Personal Data Market Optimization Pricing Model Based on Privacy Level." Information 10, no. 4 (April 3, 2019): 123. http://dx.doi.org/10.3390/info10040123.

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In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due to privacy issues, access to such data is limited. Given the business opportunities that have gaps between demand and supply, we consider establishing a private data market to resolve supply and demand conflicts. While there are many challenges to building such a data market, we only focus on three technical challenges: (1) How to provide a fair trading mechanism between data providers and data platforms? (2) What is the consumer’s attitude toward privacy data? (3) How to price personal data to maximize the profit of the data platform? In this paper, we first propose a compensation mechanism based on the privacy attitude of the data provider. Second, we analyze consumer self-selection behavior and establish a non-linear model to represent consumers’ willingness to pay (WTP). Finally, we establish a bi-level programming model and use genetic simulated annealing algorithm to solve the optimal pricing problem of personal data. The experimental results show that multi-level privacy division can meet the needs of consumers and maximize the profit of data platform.

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Wu, Zhiyan, and Jifeng Luo. "Online information privacy and price: A theoretical model and empirical tests." Information & Management 59, no. 2 (March 2022): 103583. http://dx.doi.org/10.1016/j.im.2021.103583.

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Fleisher, Lynn D., and Laura J. Cole. "Health Insurance Portability and Accountability Act is here: What price privacy?" Genetics in Medicine 3, no. 4 (August 2001): 286–89. http://dx.doi.org/10.1097/00125817-200107000-00003.

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49

Wein, Thomas. "Data Protection, Cookie Consent, and Prices." Economies 10, no. 12 (December 1, 2022): 307. http://dx.doi.org/10.3390/economies10120307.

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A legislative process is currently ongoing in the European Union to supplement the 2018 General Data Protection Regulation regarding ePrivacy regulation. The supplement is intended to complete the European data protection policy in significant areas. One addition would be for service providers on the Internet, who currently obtain the consent of their users via an opt-out provision, to always provide a paid alternative without disclosing data. This procedure is essentially aimed at overcoming “cookie consent fatigue”, which can be observed in many cases. A simple economic exchange model shows that users, as data subjects, are basically faced with the choice of paying a monetary price for a service that will also preserve their privacy or using Internet services “for free” while negating data privacy preferences. The individual demand for data privacy coincides with the socially optimal demand only if there is effective competition in the markets for data and Internet services and if users are sufficiently informed. In an online laboratory experiment with students of the Leuphana University of Lueneburg, a between-subjects design was applied in which the control group only had the option to either “pay” for the use of the artificial intelligence DeepL via cookies by surrendering data or to abstain from the service altogether, with the two treatment groups additionally given the option to use DeepL in exchange for a monetary fee so that privacy was not violated. To be tested was whether the “monetary price for privacy” option better reflected users’ privacy preferences than the current cookie opt-out solution. The results show that it was much less common for DeepL to be remunerated with the disclosure of data and less common for DeepL to be waived entirely.

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Halliday, Daniel. "Pay Transparency and Labor Market Justice." Law in Context. A Socio-legal Journal 37, no. 2 (August 28, 2021): 27–36. http://dx.doi.org/10.26826/law-in-context.v37i2.147.

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I argue that a general initial case for pay transparency can be made given the role played by transparency of information about prices in bringing markets closer to the ideal of competition or equilibrium price. This initial case might then be limited or enhanced depending on more specific considerations about the status of information about pay in particular. Privacy considerations seem to count against pay transparency, but I argue here that the context of pay information lacks some features present in other contexts in which appeals to privacy have force. Building on work by Estlund, Moriarty, Caulfield, and others, I argue that pay transparency may be favoured by considerations relating to personal autonomy in labour markets. Finally, I argue that pay transparency may contribute towards the realization of conditions of publicity, particularly relating to the value of citizens’ assurance about each other’s tax compliance.

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