Online information privacy and price: A theoretical model and empirical tests

Information privacy has become an increasingly critical concern for many individuals, especially in the context of the increased collection, analysis, and visualization of personal information. Despite the importance of privacy in online transactions, most studies investigated the effects of privacy concerns on stated intentions. This study theoretically analyzes and empirically evaluates the link between privacy and price. In the proposed model, consumers are differentiated not only by their preferences for privacy but also by search costs. For simplicity, stores in the proposed model differ in terms of the price charged and the privacy protection level offered. As derived from the theoretical model and empirical validation, better privacy protection does not necessarily lead to the highest price, though it does allow stores to charge a premium price. Moreover, the price premium is higher if more consumers remain uninformed, indicating that uninformed consumers exert negative impacts on informed consumers.     

具有网络外部效应的三度价格歧视研究

通过建立两厂商和两子市场的两阶段博弈模型,分析具有网络外部效应的寡头竞争市场厂商实施三度歧视定价的产出、价格和社会福利问题,研究结果表明:厂商通过歧视定价提高了强市场的价格,相应降低了弱市场的价格,虽然总产出没有改变,但却减少了社会总福利.     

Generalization-based privacy preservation and discrimination prevention in data publishing and mining

Living in the information society facilitates the automatic collection of huge amounts of data on individuals, organizations, etc. Publishing such data for secondary analysis (e.g. learning models and finding patterns) may be extremely useful to policy makers, planners, marketing analysts, researchers and others. Yet, data publishing and mining do not come without dangers, namely privacy invasion and also potential discrimination of the individuals whose data are published. Discrimination may ensue from training data mining models (e.g. classifiers) on data which are biased against certain protected groups (ethnicity, gender, political preferences, etc.). The objective of this paper is to describe how to obtain data sets for publication that are: (i) privacy-preserving; (ii) unbiased regarding discrimination; and (iii) as useful as possible for learning models and finding patterns. We present the first generalization-based approach to simultaneously offer privacy preservation and discrimination prevention. We formally define the problem, give an optimal algorithm to tackle it and evaluate the algorithm in terms of both general and specific data analysis metrics (i.e. various types of classifiers and rule induction algorithms). It turns out that the impact of our transformation on the quality of data is the same or only slightly higher than the impact of achieving just privacy preservation. In addition, we show how to extend our approach to different privacy models and anti-discrimination legal concepts.     

Automated buyer profiling control based on human privacy attitudes

In e-commerce applications, vendors can construct detailed profiles about customers’ preferences, which is known as buyer profiling. These profiles can then be used by vendors in order to perform practices such as price discrimination, poor judgment, etc. The use of pseudonyms and, specially, changing pseudonyms from time to time are known to minimize profiling, minimizing the capacity of vendors to perform such practices in turn. Although there are some frameworks and tools that support pseudonym change, there are few proposals that suggest or directly change the pseudonym in an automated fashion. Instead, users are usually provided with the mechanisms to change pseudonyms but without any advise on when they should actually use these mechanisms. In this paper, we present an approach to control buyer profiling by means of automated pseudonym changes performed according to human privacy attitudes. We also present an application scenario and an evaluation of our proposal.     

Improving Customer Retention in E-Commerce through a Secure and Privacy-Enhanced Loyalty System

Loyalty systems provide an interesting possibility for vendors in customer relationship management. This holds for both real world and online vendors. Many vendors apply loyalty systems to collect customer-specific data that may be exploited for many reasons, e.g., price discrimination and direct marketing. As a consequence, beside some potential benefits of a loyalty system, customers may also fear an invasion of privacy, and thus often refuse to participate in such programs. Thus, a vendor may have problems to turn privacy sensitive people into loyal customers using a typical loyalty system. In this paper, we present two variants of a privacy-friendly loyalty system to be used by online vendors for issuing loyalty points. The systems prevent vendors from exploiting data for the creation of customer profiles by providing unconditional unlinkability of loyalty points with regard to purchases. We propose a simple token-based approach and a counter-based approach which is much more efficient while preserving the privacy and security properties. Furthermore, the counter-based loyalty system prevents pooling of loyalty points which were issued to distinct customers. Matthias Enzmann received his diploma in computer science from the Technical University of Darmstadt, Germany, in 1999. In 1996, he started working with the TKT institute of GMD - German National Research Centre for Information Technology GmbH which in 2001 became Fraunhofer Institute for Secure Telecooperation due to the merger of GMD and Fraunhofer Gesellschaft. Since 1999 he holds the position of a regular researcher at Fraunhofer SIT. Currently, his research interests focus on privacy protection in electronic business processes, agent based mediation, and pseudonym systems. Markus Schneider received his diploma in electrical engineering with specialization on communications engineering. Afterwards, he started to work in the area of information and communication security and received his Doctor degree in electrical engineering. Currently, he is with the Fraunhofer Institute for Secure Telecooperation (SIT) in Darmstadt, Germany. His research interests include the development and application of security technologies in communications, and security and privacy issues in electronic business processes.     

Data privacy as a tool to combat discrimination?

This article will show how data privacy is legally recognized and treated in Japan and argue in particular how it can (not) play a role as a tool to combat discrimination in the workplace. The first part will depict a brief history of the development of the ‘right to privacy’ in Japan. This part will also take a look at the national Act on Protection of Personal Information and its complicated enforcement, paying attention to the general legal culture in Japan. The second part will observe other laws which are related to personal data protection and legal protection against discrimination. At the end of this article the author will make a comment on the Japanese data privacy law and its desirable development.     

Price versus privacy: an experiment into the competitive advantage of collecting less personal information

In previous privacy studies, consumers have reported their unease with online retailers that collect a lot of personal data. Consumers claim they will switch to alternative providers or cancel transactions if data collection is deemed excessive. Therefore, privacy appears to be a competitive factor in electronic commerce. This paper describes a study which quantifies the degree to which privacy is a competitive advantage for online retailers. In an experiment, we offered 225 participants the option to purchase one DVD from one of two online stores. Throughout the study, one online shop asked for more invasive personal data—as confirmed by an exit-questionnaire. In the test treatment, the privacy-invasive store sold DVDs for one Euro less than the other, and in the control treatment, both stores sold DVDs for the same price. Across both treatments, 74 participants made a purchase and had the DVD they bought delivered. In our study we found that, when the price of DVDs was the same between both stores, the shop asking for less personal data did not amass the entire market. When consumers were offered a trade-off between price and privacy, the vast majority of customers chose to buy from the cheaper, more privacy-invasive, firm; this firm got both a larger market share and higher revenue. The cheaper shop generated strong dissatisfaction with their privacy practises; in contrast, consumers of the more expensive store displayed only weak dissatisfaction with price. We established the validity of our analysis by checking users made informed choices, and did not select one firm over the other due to hasty decision-making or ordering effects. We found no support for either a materialistic lifestyle nor the quest for immediate gratification as to why customers chose the cheaper but privacy-unfriendly store.     

基于价格不经意传输的隐私保护交易方案

针对近年来电子商务中出现的一类特殊商品——数字产品,如电影、软件、音乐等,在对基于价格的不经意传输协议和利用RSA签名的基础上,结合同态加密、承诺方案和零知识证明协议,设计一种实现隐私保护的数字产品交易方案,以达到隐藏消费者的交易记录和保护消费者隐私的目的。     

Preserving location privacy without exact locations in mobile services

Privacy preservation has recently received considerable attention in location-based services (LBSs). A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. However, most existing cloaking approaches assume that mobile users are trusted. And exact locations are required to protect location privacy, which is exactly the information mobile users want to hide. In this paper, we propose a p-anti-conspiration privacy model to anonymize over semi-honest users. Furthermore, two k*NNG-based cloaking algorithms, vk*NNCA and ek*NNCA, are proposed to protect location privacy without exact locations. The efficiency and effectiveness of the proposed algorithms are validated by a series of carefully designed experiments. The experimental results show that the price paid for location privacy protection without exact locations is small.     

Privacy protection in pervasive systems: State of the art and technical challenges

Pervasive and mobile computing applications are dramatically increasing the amount of personal data released to service providers as well as to third parties. Data includes geographical and indoor positions of individuals, their movement patterns as well as sensor-acquired data that may reveal individuals’ physical conditions, habits, and, in general, information that may lead to undesired consequences like unsolicited advertisement or more serious ones like discrimination and stalking.In this survey paper, at first we consider representative classes of pervasive applications, and identify the requirements they impose in terms of privacy and trade-off with service quality. Then, we review the most prominent privacy preservation approaches, we discuss and summarize them in terms of the requirements.Finally, we take a more holistic view of the privacy problem by discussing other aspects that turn out to be crucial for the widespread adoption of privacy enhancing technologies. We discuss technical challenges like the need for tools augmenting the awareness of individuals and to capture their privacy preferences, as well as legal and economic challenges. Indeed, on one side privacy solutions must comply to ethical and legal requirements, and not prevent profitable business models, while on the other side it is unlikely that privacy preserving solutions will become practical and effective without new regulations.     

Valuating privacy

In several experimental auctions, participants put a dollar value on private information before revealing it to a group. An analysis of results show that a trait's desirability in relation to the group played a key role in the amount people demanded to publicize private information. Because people can easily obtain, aggregate, and disperse personal data electronically, privacy is a central concern in the information age. This concern is clear in relation to financial data and genetic information, both of which can lead to identity abuse and discrimination. However, other relatively harmless information can also be abused, including a person's gender, salary, age, marital status, or shopping preferences. What's unclear is whether it's the fear of such abuse that actually causes people's stated hesitance to reveal their data. Our hypothesis - and the motivation for our study - is that people reveal information when they feel that they're somewhat typical or positively atypical compared to the target group. To test this hypothesis, we conducted experiments that elicit the value people place on their private data. We found, with great significance (more than 95 percent statistical confidence) that a linear relationship exists between an individual's belief about a trait and the value he or she places on it. That is, the less desirable the trait, the greater the price a person demands for releasing the information. Furthermore, we found that small deviations in a socially positive direction are associated with a lower asking price.     

An uncertain price discrimination model in labor market

This paper investigates an uncertain price discrimination problem in labor market, in which the employee’s capability is his/her private information and characterized as an uncertain variable. To maximize the employer’s welfare, an uncertain price discrimination model in labor market is established. Moreover, a crisp equivalent form of the model is given, and the analytic solution is therefore obtained by employing variational method. This optimal solution shows that both the productivity and the wage are increasing with respect to the employee’s capability.     

信息不对称条件下寡头集团转移定价决策分析

研究信息不对称条件下存在中间产品外部市场的寡头集团转移定价决策问题.在不完全竞争中间产品外部市场条件下,当集团之间信息不对称时,对中间产品内外部实行单一定价,转移价格将大于中间产品的边际成本;对中间产品内外部实行差别定价时,转移价格则等于中间产品的边际成本.通过对两种策略的比较可知,寡头集团实行差别定价还是单一定价,取决于中间产品外部市场和最终产品市场的需求价格弹性.     

Facebook and Online Privacy: Attitudes,Behaviors, and Unintended Consequences

This article investigates Facebook users' awareness of privacy issues and perceived benefits and risks of utilizing Facebook. Research found that Facebook is deeply integrated in users' daily lives through specific routines and rituals. Users claimed to understand privacy issues, yet reported uploading large amounts of personal information. Risks to privacy invasion were ascribed more to others than to the self. However, users reporting privacy invasion were more likely to change privacy settings than those merely hearing about others' privacy invasions. Results suggest that this lax attitude may be based on a combination of high gratification, usage patterns, and a psychological mechanism similar to third‐person effect. Safer use of social network services would thus require changes in user attitude.     

Google, Profiling, and Privacy

Antitrust questions put a useful spotlight on the privacy issues of online profiling by those who sell advertising on the Web. These questions apply not just to Google but to any site that uses personal information and behavior data to develop and use individual users' profiles. But simply assuring advertisers a fair marketplace won't be sufficient to guarantee user privacy. We know that many users are willing to give up some privacy protection for short-term convenience, but we shouldn't conclude from these chokes that privacy means the sum total of whatever people end up choosing.     

一种简单高效的密封式电子拍卖方案

针对目前大多数的电子拍卖方案都是假设存在一个可信第三方,使得电子拍卖的安全性有所降低的问题,提出一个基于不可信第三方的密封式电子拍卖方案。采用数字签名技术对竞拍者的身份进行验证,确保竞拍者身份的隐私性。在计算成交价时,基于离散对数求解的困难性,对竞拍价的二进制长度进行加密封装,保证竞拍价的秘密性以及结果的正确性。分析结果证明,该方案设计简单,安全性较高,在计算效率上相对于现有多数电子拍卖方案有较大的提高。     

基于区块链的密封式投标拍卖方案

随着互联网技术的飞速发展,传统拍卖正逐渐转变为电子拍卖,其中隐私保护越来越受到关注。针对当前电子投标拍卖系统中出现的问题,如竞买人隐私存在被泄露的风险、第三方拍卖中心的费用昂贵、第三方拍卖中心可能与竞买人勾结等,提出一种基于区块链智能合约技术的密封式投标拍卖方案。该方案充分利用区块链的去中心化、防篡改和可信赖性等特征构建了一个无第三方的拍卖环境,并通过区块链上的安全保证金策略约束竞买人的行为,从而提高密封式电子拍卖的安全性。同时该方案利用Pedersen承诺保护竞买人的竞拍价格不被泄露,并通过Bulletproofs零知识证明协议验证中标价格的正确性。安全性分析和实验结果表明,提出的拍卖方案满足安全性要求,各个阶段的时间消耗均在可接受范围内,满足日常拍卖要求。     

Auctions and Differential Pricing: Optimal Seller and Bidder Strategies in Second-chance Offers

The second chance offer is a common seller practice on eBay. It consists of price discrimination against the losing bidder, who is offered an identical item at the value of his or her highest bid. Prior work has shown that, if the goods are private-value goods and price discrimination is certain, rational bidders can anticipate it and accordingly modify their bidding strategies; this results in revenue loss for the seller. This paper hence examines the impact of randomized price discrimination. It examines a similar, more general problem: a seller has k items of the private-value good. They are sold to N bidders in a two-stage game. The first stage is a sealed-bid first-price auction with N bidders. The second stage is a take-it-or-leave-it offer to each of k − 1 losing bidders; randomized between a fixed-price offer and a second-chance offer. Showing that analytic techniques do not provide complete solutions because bidding strategies are not always monotonic increasing, this paper uses genetic algorithm simulations to determine the Bayesian (near-Nash) equilibrium strategies for bidders and sellers, for N = 8 and different values of k. It finds that price discrimination is beneficial to the seller when k is small, as item scarcity increases competition among bidders. The paper’s use of randomized seller strategies and genetic algorithm simulations is unique in the study of the second-chance offer.     

Security, Privacy, and the Role of Law

US President Barack Obama promised a "new comprehensive approach" to cybersecurity and guaranteed to preserve "personal privacy and civil liberties," but the administration has stopped short of committing to the legal changes necessary to protect either information infrastructure or privacy. This tendency to undervalue law as a tool for enhancing both security and individual privacy is shared with other governments. Sound cybersecurity policy requires better incentives to secure data and systems, and those incentives will emerge, at least in part, from legal requirements. Similarly, serious efforts to protect against cyberthreats will compromise privacy and other civil rights unless those rights are protected by law.     

Fuzzy Facebook privacy boundaries: Exploring mediated lurking,vague-booking,and Facebook privacy management

Managing privacy in the interconnected digital interaction environment of Facebook can be a complex yet vital endeavor. This study utilizes the theoretical framework of Communication Privacy Management (CPM) theory to explore relationships between concern about mediated lurking, strategic ambiguity (or vague-booking) on Facebook, and Facebook privacy management. The study explores three hypotheses. Overall, 383 participants completed an online survey instrument. Greater concern about mediated lurking was related to more Facebook privacy management. More frequent use of strategic ambiguity on Facebook was related to less Facebook privacy management. Individuals who were more highly concerned about mediated lurking were also more likely to employ more frequent use of strategic ambiguity on Facebook. Men engaged in significantly greater use of strategic ambiguity on Facebook and enacted significantly less Facebook privacy management than did women. Implications and future research related to Facebook privacy management from a CPM perspective are explored.