In the last few years, Header Bidding (HB) has gained popularity among web publishers and is challenging the status quo in the ad ecosystem. Contrary to the traditional waterfall standard, HB aims to give back control of the ad inventory to publishers, increase transparency, fairness and competition among advertisers, thus, resulting in higher ad-slot prices. Although promising, little is known about this new ad-tech protocol: How does it work internally and what are the different implementations of HB? What is the performance overhead, and how does it affect the user experience? Does it, indeed, provide higher revenues to publishers than the waterfall model? Who are the dominating entities in this new protocol? To respond to all these questions and shed light on this new, buzzing ad-technology, we design and implement HBDetector: a holistic HB detection mechanism that can capture HB auctions independently of the implementation followed in a website. By running HBDetector across the top 35,000 Alexa websites, we collect and analyze a dataset of 800k auctions. Our results show that: (i) 14.28 of the top Alexa websites utilize HB. (ii) Publishers tend to collaborate mostly with a relatively low number of demand partners, which are already big players in waterfall standard, (iii) HB latency can be significantly higher than waterfall, with up to 3x latency in the median cases.
User data and their economics have long been an interesting topic and attracted a considerable body of research @cite_25 @cite_18 @cite_0 @cite_9 @cite_19 @cite_2 @cite_29 @cite_39 @cite_38 @cite_6 @cite_13 . In particular, in @cite_19 , Acquisti al discuss the value of privacy after defining two concepts (i) : the monetary amount users are willing to pay to protect their privacy, and (ii) : the compensation that users are willing to accept for their privacy loss. In two user-studies @cite_0 @cite_9 authors measure how much users value their own offline and online personal data, and consequently how much they would sell them to advertisers. @cite_2 , authors propose transactional'' privacy to allow users to decide what personal information can be released and receive compensation from selling them.
@cite_38 @cite_18 @cite_9 @cite_29 @cite_6 @cite_39 @cite_0 @cite_19 @cite_2 @cite_13 @cite_25
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AbstractUnderstanding the value that individuals assign to the protection of their personal data is of great importance for business, law, and public policy. We use a field experiment informed by behavioral economics and decision research to investigate individual privacy valuations and find evidence of endowment and order effects. Individuals assigned markedly different values to the privacy of their data depending on (1) whether they were asked to consider how much money they would accept to disclose otherwise private information or how much they would pay to protect otherwise public information and (2) the order in which they considered different offers for their data. The gap between such values is large compared with that observed in comparable studies of consumer goods. The results highlight the sensitivity of privacy valuations to contextual, nonnormative factors. The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers. Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of the individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties. As awareness of the value of personal data increases, so has the drive to compensate the end-user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means. In this article we propose a theoretical framework for assigning prices to noisy query answers as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the pricing function and micropayments, and characterize valid solutions. Most online service providers offer free services to users and in part, these services collect and monetize personally identifiable information (PII), primarily via targeted advertisements. Against this backdrop of economic exploitation of PII, it is vital to understand the value that users put to their own PII. Although studies have tried to discover how users value their privacy, little is known about how users value their PII while browsing, or the exploitation of their PII. Extracting valuations of PII from users is non-trivial - surveys cannot be relied on as they do not gather information of the context where PII is being released, thus reducing validity of answers. In this work, we rely on refined Experience Sampling - a data collection method that probes users to valuate their PII at the time and place where it was generated in order to minimize retrospective recall and hence increase measurement validity. For obtaining an honest valuation of PII, we use a reverse second price auction. We developed a web browser plugin and had 168 users - living in Spain - install and use this plugin for 2 weeks in order to extract valuations of PII in different contexts. We found that users value items of their online browsing history for about ∈7 ( 10USD), and they give higher valuations to their offline PII, such as age and address (about 25∈ or 36USD). When it comes to PII shared in specific online services, users value information pertaining to financial transactions and social network interactions more than activities like search and shopping. No significant distinction was found between valuations of different quantities of PII (e.g. one vs. 10 search keywords), but deviation was found between types of PII (e.g. photos vs. keywords). Finally, the users' preferred goods for exchanging their PII included money and improvements in service, followed by getting more free services and targeted advertisements. In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data. This paper introduces results of a study into the value of location privacy for individuals using mobile devices. We questioned a sample of over 1200 people from five EU countries, and used tools from experimental psychology and economics to extract from them the value they attach to their location data. We compare this value across national groups, gender and technical awareness, but also the perceived difference between academic use and commercial exploitation. We provide some analysis of the self-selection bias of such a study, and look further at the valuation of location data over time using data from another experiment. Monetizing personal information is a key economic driver of online industry. End-users are becoming more concerned about their privacy, as evidenced by increased media attention. This paper proposes a mechanism called 'transactional' privacy that can be applied to personal information of users. Users decide what personal information about themselves is released and put on sale while receiving compensation for it. Aggregators purchase access to exploit this information when serving ads to a user. Truthfulness and efficiency, attained through an unlimited supply auction, ensure that the interests of all parties in this transaction are aligned. We demonstrate the effectiveness of transactional privacy for web-browsing using a large mobile trace from a major European capital. We integrate transactional privacy in a privacy-preserving system that curbs leakage of information. These mechanisms combine to form a market of personal information that can be managed by a trusted third party. The aim of this research was to understand what affects people's privacy preferences in smartphone apps. We ran a four-week study in the wild with 34 participants. Participants were asked to answer questions, which were used to gather their personal context and to measure their privacy preferences by varying app name and purpose of data collection. Our results show that participants shared the most when no information about data access or purpose was given, and shared the least when both of these details were specified. When just one of either purpose or the requesting app was shown, participants shared less when just the purpose was specified than when just the app name was given. We found that the purpose for data access was the predominant factor affecting users' choices. In our study the purpose condition vary from being not specified, to vague to be very specific. Participants were more willing to disclose data when no purpose was specified. When a vague purpose was shown, participants became more privacy-aware and were less willing to disclose their information. When specific purposes were shown participants were more willing to disclose when the purpose for requesting the information appeared to be beneficial to them, and shared the least when the purpose for data access was solely beneficial to developers. We initiate the study of markets for private data, through the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing. In this paper, we propose to build such a theory. Specifically, we consider a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic. The analyst wishes to obtain an accurate estimate cheaply, while the owners of the private data experience some cost for their loss of privacy, and must be compensated for this loss. Agents are selfish, and wish to maximize their profit, so our goal is to design truthful mechanisms. Our main result is that such problems can naturally be viewed and optimally solved as variants of multi-unit procurement auctions. Based on this result, we derive auctions which are optimal up to small constant factors for two natural settings: When the data analyst has a fixed accuracy goal, we show that an application of the classic Vickrey auction achieves the analyst's accuracy goal while minimizing his total payment. When the data analyst has a fixed budget, we give a mechanism which maximizes the accuracy of the resulting estimate while guaranteeing that the resulting sum payments do not exceed the analyst's budget. In both cases, our comparison class is the set of envy-free mechanisms, which correspond to the natural class of fixed-price mechanisms in our setting. In both of these results, we ignore the privacy cost due to possible correlations between an individual's private data and his valuation for privacy itself. We then show that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy. This is nevertheless an important issue, and modeling it correctly is one of the many exciting directions for future work. In the information realm, loss of privacy is usually associated with failure to control access to information, to control the flow of information, or to control the purposes for which information is employed. Differential privacy arose in a context in which ensuring privacy is a challenge even if all these control problems are solved: privacy-preserving statistical analysis of data. The problem of statistical disclosure control – revealing accurate statistics about a set of respondents while preserving the privacy of individuals – has a venerable history, with an extensive literature spanning statistics, theoretical computer science, security, databases, and cryptography (see, for example, the excellent survey [1], the discussion of related work in [2] and the Journal of Official Statistics 9 (2), dedicated to confidentiality and disclosure control). This long history is a testament the importance of the problem. Statistical databases can be of enormous social value; they are used for apportioning resources, evaluating medical therapies, understanding the spread of disease, improving economic utility, and informing us about ourselves as a species. The data may be obtained in diverse ways. Some data, such as census, tax, and other sorts of official data, are compelled; others are collected opportunistically, for example, from traffic on the internet, transactions on Amazon, and search engine query logs; other data are provided altruistically, by respondents who hope that sharing their information will help others to avoid a specific misfortune, or more generally, to increase the public good. Altruistic data donors are typically promised their individual data will be kept confidential – in short, they are promised “privacy.” Similarly, medical data and legally compelled data, such as census data, tax return data, have legal privacy mandates. In our view, ethics demand that opportunistically obtained data should be treated no differently, especially when there is no reasonable alternative to engaging in the actions that generate the data in question. The problems remain: even if data encryption, key management, access control, and the motives of the data curator Suppose that party A collects private information about its users, where each user's data is represented as a bit vector. Suppose that party B has a proprietary data mining algorithm that requires estimating the distance between users, such as clustering or nearest neighbors. We ask if it is possible for party A to publish some information about each user so that B can estimate the distance between users without being able to infer any private bit of a user. Our method involves projecting each user's representation into a random, lower-dimensional space via a sparse Johnson-Lindenstrauss transform and then adding Gaussian noise to each entry of the lower-dimensional representation. We show that the method preserves differential privacy---where the more privacy is desired, the larger the variance of the Gaussian noise. Further, we show how to approximate the true distances between users via only the lower-dimensional, perturbed data. Finally, we consider other perturbation methods such as randomized response and draw comparisons to sketch-based methods. While the goal of releasing user-specific data to third parties is more broad than preserving distances, this work shows that distance computations with privacy is an achievable goal.