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 .

Cyclic codes form an important subclass of linear block codes. These codes are attractive for two reasons: first, encoding and syndrome computation can be implemented easily by using simple shift-registers with linear feedback connections, namely, linear feedback shift-registers (LFSRs); and second, because they have considerable inherent algebraic structure, it is possible to devise various practical algorithms for decoding them. Cyclic codes have been widely used in communication and storage systems for error control. They are particularly efficient for error detection.

Finite fields have been applied to construct error-correcting codes for reliable information transmission and data storage [–] since the late 1950s. These codes are commonly called algebraic codes, which have nice structures and large minimum distances. The most well-known classical algebraic codes are BCH and RS codes presented inandwhich can be decoded with the elegant hard-decision Berlekamp–Massey iterative algorithm.

Polar codes, discovered by Arikan [] in 2009, form a class of codes which provably achieve the capacity for a wide range of channels. Construction, encoding, and decoding of these codes are based on the phenomenon of channel polarization. In this chapter, we introduce polar codes from an algebraic point of view.

Apart from the construction of BCH, RS, finite-geometry, and RM codes based on finite fields and finite geometries, there are other methods (or techniques) for constructing long powerful codes from good short codes.