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This chapter explains how the collected data can be processed with the aim of extracting meaningful measures, and how statistical analysis can be used to support significant conclusions. This chapter first introduces common quantitative measures for transparency research, including measures for tracking, privacy, fairness, and similarity. To compute most of these measures, data need to be preprocessed to extract the response variables of interest from the raw collected data, for example using simple transformations or heuristics, machine learning classifiers or natural language processing, or static and dynamic analysis methods for mobile apps. Finally, the chapter explains statistical methods that allow to make meaningful and statistically significant statements about the behavior of the response variables in the experiment.
This chapter examines the formidable challenges that remain in transparency research, not least due to the rapid evolution of technology. This chapter highlights four areas of challenges: methodological challenges that call for new methods for transparency research; open research questions related to existing systems; research questions related to new and emerging systems such as the internet of things; and challenges related to systems that are pervasively embedded into real-world systems and infrastructure, such as smart cites.
This chapter discusses results for user-facing services that show whether the services show biases towards specific groups of users, whether they comply with policies, laws, and regulations, and how they use user data in providing their services. The chapter first focuses on network-level services, such as server-side blocking and the provision of wireless internet access. Then, the chapter discusses web-based services, including privacy policies, search, social networks, and e-commerce. The chapter closes by discussing results for mobile services, such as the characteristics of app stores, third-party libraries, and apps.
This chapter explains how corporate surveillance works on a technical level: how individual users can be tracked across their use of web and mobile services, for example through stateful tracking with cookies or stateless tracking with fingerprinting; how information collected through tracking is consolidated in comprehensive user profiles; how analytics services contribute to tracking and profiling; and how advertising technology works, including ad targeting and ad sales.
This chapter explains how transparency research can lead to real-world change. After introducing impacts that are commonly realized by transparency research, the chapter systematizes types of impacts and explains a process for planning impact throughout a research project. Finally, the chapter explains, using real-world examples, how various groups of stakeholders can be engaged, including the public, policy-makers, courts, regulatory bodies, standardization bodies, non-governmental organizations, publishers, and developers.
This chapter explains how to design experiments to study black-box corporate surveillance systems. The chapter first examines the kinds of research questions that can be asked about corporate surveillance systems. Then, it describes different high-level study designs for transparency research, followed by a look at longitudinal studies and how they can be conducted. After examining the challenges that transparency researchers face in designing these experiments, the chapter focuses on input variables that are influenced and varied during an experiment, variables that are outside the experimenter's influence, and variables that are measured (response or output variables).
This chapter focuses on the inner workings of networked services – what technologies they use and how they work – which will enable a deeper understanding of the methods used for corporate surveillance. The chapter first introduces the internet protocol suite and its most important protocols, and then explains the systems and languages used to deliver web-based content and mobile content.
This chapter examines findings from transparency research that shed light on the methods used for corporate surveillance, including tracking, profiling, analytics, and advertising. The chapter focuses on key results obtained for the research questions described in chapter 4 and explains the experimental designs used to achieve them.
This chapter examines the arms race between corporate surveillance and the countermeasures that allow users to defend themselves against advertising, tracking, and profiling. The chapter first explains ad blockers as the most common countermeasure, and shows how the industry is using anti-ad blockers to block ad blocker users. The chapter then discusses specialized blockers for tracking and fingerprinting, as well as countermeasures based on obfuscation and tools that aim to increase user awareness, before closing with a discussion of countermeasures for mobile devices and applications.
This chapter describes methods for executing the designed experiment and recording the response variables. The ethical implications of the experiment have to be considered before starting data collection, with the aim to minimize harmful impacts. Various data sources and data collection methods are available: archival data sources, passive data collection, active data collection with methods to influence input variables, data collection from mobile apps, and data collection via crowdsourcing. The chapter also describes methods to store the collected data.
News headlines about privacy invasions, discrimination, and biases discovered in the platforms of big technology companies are commonplace today, and big tech's reluctance to disclose how they operate counteracts ideals of transparency, openness, and accountability. This book is for computer science students and researchers who want to study big tech's corporate surveillance from an experimental, empirical, or quantitative point of view and thereby contribute to holding big tech accountable. As a comprehensive technical resource, it guides readers through the corporate surveillance landscape and describes in detail how corporate surveillance works, how it can be studied experimentally, and what existing studies have found. It provides a thorough foundation in the necessary research methods and tools, and introduces the current research landscape along with a wide range of open issues and challenges. The book also explains how to consider ethical issues and how to turn research results into real-world change.