On Web User Tracking: How Third-Party Http Requests Track Users' Browsing Patterns for Personalised Advertising

On today's Web, users trade access to their private data for content and services. Advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users' actions and tastes to suggest a range of products to buy. It follows that, while surfing the Web users leave traces regarding their identity in the form of activity patterns and unstructured data. We analyse how advertising networks build user footprints and how the suggested advertising reacts to changes in the user behaviour.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0653

Constitutive surveillance and social media

Starting from the premise that surveillance is the ‘dominant organising practice’ of our time (Lyon et al 2012: 1), this thesis establishes a framework of ‘constitutive surveillance’ in relation to social media, taking Facebook as its key example. Constitutive surveillance is made up of four forms: economic, political, lateral, and oppositional surveillance. These four surveillance forms – and the actors who undertake them – intersect, compound, and confront one another in the co-production of social media spaces. The framework of constitutive surveillance is structured around a Foucauldian understanding of power, and the thesis shows how each surveillance form articulates strategies of power for organising, administering, and subjectifying populations. After outlining the four surveillance forms, each chapter unpacks the relationship of one form to social media, building throughout the thesis an extensive critical framework of constitutive surveillance

Information Privacy and the Inference Economy

Information privacy is in trouble. Contemporary information privacy protections emphasize individuals’ control over their own personal information. But machine learning, the leading form of artificial intelligence, facilitates an inference economy that pushes this protective approach past its breaking point. Machine learning provides pathways to use data and make probabilistic predictions—inferences—that are inadequately addressed by the current regime. For one, seemingly innocuous or irrelevant data can generate machine learning insights, making it impossible for an individual to anticipate what kinds of data warrant protection. Moreover, it is possible to aggregate myriad individuals’ data within machine learning models, identify patterns, and then apply the patterns to make inferences about other people who may or may not be part of the original dataset. The inferential pathways created by such models shift away from “your” data and towards a new category of “information that might be about you.” And because our law assumes that privacy is about personal, identifiable information, we miss the privacy interests implicated when aggregated data that is neither personal nor identifiable can be used to make inferences about you, me, and others. This Article contends that accounting for the power and peril of inferences requires reframing information privacy governance as a network of organizational relationships to manage—not merely a set of dataflows to constrain. The status quo magnifies the power of organizations that collect and process data, while disempowering the people who provide data and who are affected by data-driven decisions. It ignores the triangular relationship among collectors, processors, and people and, in particular, disregards the codependencies between organizations that collect data and organizations that process data to draw inferences. It is past time to rework the structure of our regulatory protections. This Article provides a framework to move forward. Accounting for organizational relationships reveals new sites for regulatory intervention and offers a more auspicious strategy to contend with the impact of data on human lives in our inference economy