text: 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.
  @cite_38: 
  @cite_18: The OECD, the European Union and other public and private initiatives are claiming for the necessity of tools that create awareness among Internet users about the monetary value associated to the commercial exploitation of their online personal information. This paper presents the first tool addressing this challenge, the Facebook Data Valuation Tool (FDVT). The FDVT provides Facebook users with a personalized and real-time estimation of the revenue they generate for Facebook. Relying on the FDVT, we are able to shed light into several relevant HCI research questions that require a data valuation tool in place. The obtained results reveal that (i) there exists a deep lack of awareness among Internet users regarding the monetary value of personal information, (ii) data valuation tools such as the FDVT are useful means to reduce such knowledge gap, (iii) 1 3 of the users testing the FDVT show a substantial engagement with the tool.
  @cite_9: 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.
  @cite_29: 
  @cite_6: Online advertising drives the economy of the World Wide Web. Modern websites of any size and popularity include advertisements to monetize visits from their users. To this end, they assign an area of their web page to an advertising company (so called ad exchange) that will use it to display promotional content. By doing this, the website owner implicitly trusts that the advertising company will offer legitimate content and it will not put the site's visitors at risk of falling victims of malware campaigns and other scams. In this paper, we perform the first large-scale study of the safety of the advertisements that are encountered by the users on the Web. In particular, we analyze to what extent users are exposed to malicious content through advertisements, and investigate what are the sources of this malicious content. Additionally, we show that some ad exchanges are more prone to serving malicious advertisements than others, probably due to their deficient filtering mechanisms. The observations that we make in this paper shed light on a little studied, yet important, aspect of advertisement networks, and can help both advertisement networks and website owners in securing their web pages and in keeping their visitors safe.
  @cite_39: Third-party services form an integral part of the mobile ecosystem: they allow app developers to add features such as performance analytics and social network integration, and to monetize their apps by enabling user tracking and targeted ad delivery. At present users, researchers, and regulators all have at best limited understanding of this third-party ecosystem. In this paper we seek to shrink this gap. Using data from users of our ICSI Haystack app we gain a rich view of the mobile ecosystem: we identify and characterize domains associated with mobile advertising and user tracking, thereby taking an important step towards greater transparency. We furthermore outline our steps towards a public catalog and census of analytics services, their behavior, their personal data collection processes, and their use across mobile apps.
  @cite_0: 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.
  @cite_19: 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.
  @cite_2: 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.
  @cite_13: 
  @cite_25: Online advertising is progressively moving towards a programmatic model in which ads are matched to actual interests of individuals collected as they browse the web. Letting the huge debate around privacy aside, a very important question in this area, for which little is known, is: How much do advertisers pay to reach an individual? In this study, we develop a first of its kind methodology for computing exactly that - the price paid for a web user by the ad ecosystem - and we do that in real time. Our approach is based on tapping on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted prices for winning bids paid by advertisers in order to place targeted ads. Our main technical contribution is a method for tallying winning bids even when they are encrypted. We achieve this by training a model using as ground truth prices obtained by running our own "probe" ad-campaigns. We design our methodology through a browser extension and a back-end server that provides it with fresh models for encrypted bids. We validate our methodology using a one year long trace of 1600 mobile users and demonstrate that it can estimate a user's advertising worth with more than 82 accuracy.
summary: 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.