Panagiotis Papadopoulos

Gonçalo Pestana, Iñigo Querejeta-Azurmendi, Panagiotis Papadopoulos et al.

Online advertising fuels the (seemingly) free internet. However, although users can access most of the web services free of charge, they pay a heavy coston their privacy. They are forced to trust third parties and intermediaries, who not only collect behavioral data but also absorb great amounts of ad revenues. Consequently, more and more users opt out from advertising by resorting to ad blockers, thus costing publishers millions of dollars in lost ad revenues. Albeit there are various privacy-preserving advertising proposals (e.g.,Adnostic, Privad, Brave Ads) from both academia and industry, they all rely on centralized management that users have to blindly trust without being able to audit, while they also fail to guarantee the integrity of the per-formance analytics they provide to advertisers. In this paper, we design and deploy THEMIS, a novel, decentralized and privacy-by-design ad platform that requires zero trust by users. THEMIS (i) provides auditability to its participants, (ii) rewards users for viewing ads, and (iii) allows advertisers to verify the performance and billing reports of their ad campaigns. By leveraging smart contracts and zero-knowledge schemes, we implement a prototype of THEMIS and early performance evaluation results show that it can scale linearly on a multi sidechain setup while it supports more than 51M users on a single-sidechain.

Manolis Chalkiadakis, Alexandros Kornilakis, Panagiotis Papadopoulos et al.

Over the past decade, we have witnessed the rise of misinformation on the Internet, with online users constantly falling victims of fake news. A multitude of past studies have analyzed fake news diffusion mechanics and detection and mitigation techniques. However, there are still open questions about their operational behavior such as: How old are fake news websites? Do they typically stay online for long periods of time? Do such websites synchronize with each other their up and down time? Do they share similar content through time? Which third-parties support their operations? How much user traffic do they attract, in comparison to mainstream or real news websites? In this paper, we perform a first of its kind investigation to answer such questions regarding the online presence of fake news websites and characterize their behavior in comparison to real news websites. Based on our findings, we build a content-agnostic ML classifier for automatic detection of fake news websites (i.e. accuracy) that are not yet included in manually curated blacklists.

Gonçalo Pestana, Iñigo Querejeta-Azurmendi, Panagiotis Papadopoulos et al.

Online advertising fuels the (seemingly) free internet. However, although users can access most websites free of charge, they need to pay a heavy cost on their privacy and blindly trust third parties and intermediaries that absorb great amounts of adrevenues and user data. This is one of the reasons users opt out from advertising by resorting ad blockers thatin turn cost publishers millions of dollars in lost adrevenues. Existing privacy-preserving advertising approaches(e.g., Adnostic, Privad, Brave Ads) from both industry and academia cannot guarantee the integrity of the performance analytics they provide to advertisers, while they also rely on centralized management that users have to trust without being able to audit. In this paper, we propose THEMIS, a novel privacy-by-design ad platform that is decentralized and requires zero trust from users. THEMIS (i) provides auditability to all participants, (ii) rewards users for viewing ads, and (iii) allows advertisers to verify the performance and billing reports of their ad campaigns. To demonstrate the feasibility and practicability of our approach, we implemented a prototype of THEMIS using a combination of smart contracts and zero-knowledge schemes. Performance evaluation results show that during adreward payouts, THEMIS can support more than 51M users on a single-sidechain setup or 153M users ona multi-sidechain setup, thus proving that THEMIS scales linearly.

Pushkal Agarwal, Sagar Joglekar, Panagiotis Papadopoulos et al.

Websites with hyper-partisan, left or right-leaning focus offer content that is typically biased towards the expectations of their target audience. Such content often polarizes users, who are repeatedly primed to specific (extreme) content, usually reflecting hard party lines on political and socio-economic topics. Though this polarization has been extensively studied with respect to content, it is still unknown how it associates with the online tracking experienced by browsing users, especially when they exhibit certain demographic characteristics. For example, it is unclear how such websites enable the ad-ecosystem to track users based on their gender or age. In this paper, we take a first step to shed light and measure such potential differences in tracking imposed on users when visiting specific party-line's websites. For this, we design and deploy a methodology to systematically probe such websites and measure differences in user tracking. This methodology allows us to create user personas with specific attributes like gender and age and automate their browsing behavior in a consistent and repeatable manner. Thus, we systematically study how personas are being tracked by these websites and their third parties, especially if they exhibit particular demographic properties. Overall, we test 9 personas on 556 hyper-partisan websites and find that right-leaning websites tend to track users more intensely than left-leaning, depending on user demographics, using both cookies and cookie synchronization methods and leading to more costly delivered ads.

Emmanouil Karampinakis, Michalis Pachilakis, Panagiotis Papadopoulos et al.

Bitcoin is the first and undoubtedly most successful cryptocurrecny to date with a market capitalization of more than 100 billion dollars. Today, Bitcoin has more than 100,000 supporting merchants and more than 3 million active users. Besides the trust it enjoys among people, Bitcoin lacks of a basic feature a substitute currency must have: stability of value. Hence, although the use of Bitcoin as a mean of payment is relative low, yet the wild ups and downs of its value lure investors to use it as useful asset to yield a trading profit. In this study, we explore this exact nature of Bitcoin aiming to shed light in the newly emerged and rapid growing marketplace of cryptocurencies and compare the investmet landscape and patterns with the most popular traditional stock market of Dow Jones. Our results show that most of Bitcoin addresses are used in the correct fashion to preserve security and privacy of the transactions and that the 24/7 open market of Bitcoin is not affected by any political incidents of the offline world, in contrary with the traditional stock markets. Also, it seems that there are specific longitudes that lead the cryptocurrency in terms of bulk of transactions, but there is not the same correlation with the volume of the coins being transferred.

Michalis Pachilakis, Panagiotis Papadopoulos, Nikolaos Laoutaris et al.

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 displayed 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.

Panagiotis Papadopoulos, Panagiotis Ilia, Evangelos P. Markatos

The recent advances of web-based cryptomining libraries along with the whopping market value of cryptocoins have convinced an increasing number of publishers to switch to web mining as a source of monetization for their websites. The conditions could not be better nowadays: the inevitable arms race between adblockers and advertisers is at its peak with publishers caught in the crossfire. But, can cryptomining be the next primary monetization model in the post advertising era of free Internet? In this paper, we respond to this exact question. In particular, we compare the profitability of cryptomining and advertising to assess the most advantageous option for a content provider. In addition, we measure the costs imposed to the user in each case with regards to power consumption, resources utilization, network traffic, device temperature and user experience. Our results show that cryptomining can surpass the profitability of advertising under specific circumstances, however users need to sustain a significant cost on their devices.

Panagiotis Papadopoulos, Nicolas Kourtellis, Pablo Rodriguez Rodriguez et al.

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.