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Identifier 000419269
Title Analyzing the impact of digital advertising on user privacy
Alternative Title Ανάλυση των επιπτώσεων της ψηφιακής διαφήμισης στην ιδιωτικότητα των χρηστών
Author Παπαδόπουλος, Παναγιώτης Ε
Thesis advisor Μαρκάτος, Ευάγγελος
Reviewer Ιωαννίδης, Σωτήρης
Δημητρόπουλος, Ξενοφώντας
Κουτρέλλης, Νικόλας
Πολυχρονάκης, Μιχάλης
Λαουτάρης, Νικόλαος
Καπραβέλος, Αλέξανδρος
Abstract Digital advertising is a multi-billion dollar business that has the power to fuel the entire free Internet. The recent years, it progressively moves towards a programmatic model in which ads are matched to actual interests of individuals collected as they browse the web. The advertiser pays a monetary cost to buy ad-space in a publisher’s medium (e.g., website) thus delivering their digital advertisement along with the publisher’s interesting content in the visitor’s display. Unlike traditional advertisements in mediums such as newspapers, TV or radio, in the digital world, the end-users are also paying a cost for the advertisement delivery. Whilst the cost on the advertiser’s side is clearly monetary, on the end-user, it includes both quantifiable costs, such as network requests and transferred bytes, and qualitative costs such as privacy loss to the ad ecosystem. Indeed, as advertisements become more and more personalized to match the users interests and become as effective as possible, more personal information about the visiting users is needed. Motivated by that, tracking companies deploy sophisticated user- tracking mechanisms retrieving any piece of information can reveal the user’s interests and preferences. Such information may include current and historical geolocations, installed apps, browsing histories, and so forth. All this information is used to form rich user profiles and large audience segments that can be shared with or sold to anyone interested (e.g., advertisers, data brokers, data management platforms, etc.) beyond the control of the users. To conduct such data markets and before performing any background user database merges, different entities perform synchronisations of the different userIDs they have set for the same users. This way they reduce the number of the different “aliases” with which they know a user, increasing this way their capability of re-identifying users when they erase their browser state (i.e., cookies) or even when they browse through VPN to preserve their privacy. Besides the continuous growth of digital advertising and its impact on our everyday lives, little we know about the flow of information within the participating companies and the interconnections between them. Motivated by that, in this dissertation, we aim to enhance the transparency in this large ecosystem and investigate the bidirectional effect between user privacy and programmatic ad-buying. In particular, we explore the impact of personalized advertising on the users privacy and anonymity given the elaborate deployed user tracking and personal data collecting techniques. We experimentally measure the user information leaks appeared while using websites and mobile apps. Based on the insight gained from these experiments, we design countermeasures to mitigate the privacy loss. Towards the opposite direction, we study how these collected user data affect the pricing dynamics of programmatic ad-auctions and how much advertisers pay to reach a user. Then, we compare the costs imposed by digital advertising to both users and advertiser for the very same delivered ad traffic. These costs include network overhead, temperature, energy consumption, loss of privacy. Finally, in an attempt to investigate pricacy-preserving alternatives for web monetization that can be completely detached from any personal data requirement, we perform a detailed analysis of the profitability and the user-side overheads of the emerging technology of web cryptomining.
Language English
Subject Personalised advertisement
Worldwibe web
Προσωποποιημένες διαφημίσεις
Issue date 2018-11-23
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
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