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.

In general, LDPC codes are classified into two categories based on their construction methods: algebraic methods and graphical methods. LDPC codes constructed based on finite geometries and finite fields are classified as algebraic LDPC codes, such as the cyclic and quasi-cyclic LDPC codes presented into .