The literature has shown that combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. This work demonstrates that a combination of a few non-PII items can be activated to nanotarget users. We demonstrate that the combination of the location and 5 rare (13 random) skills in a LinkedIn profile is enough to become unique in a user base of ∼ 970M users with a probability of 75%. The novelty is that these attributes are publicly accessible to anyone registered on LinkedIn and can be activated through advertising campaigns. We ran an experiment configuring ad campaigns using the location and skills of three of the paper’s authors, demonstrating how all the ads using ≥ 13 skills were delivered exclusively to the targeted user. We reported this vulnerability to LinkedIn, which initially ignored the problem, but fixed it as of November 2023.

Many Internet services obtain their revenue through the delivery of online advertisements based on the commercial exploitation of users’ profiles. The accuracy and size of these profiles have important implications in terms of advertisers’ campaign performance and users’ privacy. Despite the importance of auditing the profiling accuracy, very little effort has been devoted both in industry and academia. This paper presents the most comprehensive auditing effort to understand the profiling accuracy of four major online advertising platforms: Google, Facebook, Twitter, and LinkedIn. Our work unveils that less than 50% of the assigned interests are relevant. Moreover, platforms can distinguish what interests within the assigned ones are more relevant but hide this information from users and advertisers. Finally, we have proposed a very simple solution that only uses 25 general interests per user. This proposal outperforms all the analyzed platforms in terms of profile accuracy while improving users’ privacy.

The literature has shown combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. In this work, we demonstrate that the combination of the location and 6 rare (14 random) skills in a LinkedIn profile is enough to become unique in a user base of ~800M users with a probability of 75%. The novelty is these attributes are publicly accessible to anyone registered on LinkedIn and could be activated through advertising campaigns.

Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors’ knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.

The privacy of an individual is bounded by the ability of a third party to reveal their identity. Certain data items such as a passport ID or a mobile phone number may be used to uniquely identify a person. These are referred to as Personal Identifiable Information (PII) items. Previous literature has also reported that, in datasets including millions of users, a combination of several non-PII items (which alone are not enough to identify an individual) can uniquely identify an individual within the dataset. In this paper, we define a data-driven model to quantify the number of interests from a user that make them unique on Facebook. To the best of our knowledge, this represents the first study of individuals' uniqueness at the world population scale. Besides, users' interests are actionable non-PII items that can be used to define ad campaigns and deliver tailored ads to Facebook users. We run an experiment through 21 Facebook ad campaigns that target three of the authors of this paper to prove that, if an advertiser knows enough interests from a user, the Facebook Advertising Platform can be systematically exploited to deliver ads exclusively to a specific user. We refer to this practice as nanotargeting. Finally, we discuss the harmful risks associated with nanotargeting such as psychological persuasion, user manipulation, or blackmailing, and provide easily implementable countermeasures to preclude attacks based on nanotargeting campaigns on Facebook.

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.

Nowadays Internet companies that offer valuable services "for free" are becoming ubiquitous. Users benefiting from these services have to expose their personal information through these services as they utilize them. On the other hand, personal information is becoming a merchandisable commodity, venues that sell personal information by auction are emerging. One of these markets is in the form of advertising systems. Despite being a lucrative business, the hoarding of user personal information by commercial companies is a growing issue primarily because of its non-transparent nature. In this paper we present a data valuation portal that shades light on what kinds of personal information is on market and the financial value of it.

Ad networks use the behaviors of online users to associate them with preferences (features), and market these features to enable advertisers to target online users. Typical features associated with users include location, interests, gender, age, and etc. Furthemore, ad networks provide their clients with campaing creation tools to help to them to configure and run campains. In this paper, we study the pricing of ads using the ad campaing planning tools of ad networks. We develop tools to collect the suggested bid prices from two platforms: YouTube and Facebook. Analyzing these prices we find that United States is the most expensive country in both platforms. We also find that the most expensive preferences are different in YouTube and Facebook. In YouTube, the top preferences are related to Oil & Gas, while in Facebook are devices, ethnics or politics depending on the type of bidding. Finally, we do not find any price difference genders in Facebook.