This chapter covers data provenance or data lineage, the detailed history of how data was created and manipulated, as well as the process of ensuring the validity of such data by documenting the details of its origins and transformations. Data provenance is a central challenge when working with data. Computing helps but also hinders our ability to maintain records of our work with the data. The best science will result when we adopt strategies to carefully and consistently record and track the origin of data and any changes made along the way. For instance, we want to know where (by whom) a dataset was created and what was the process used to create it. Then, if there were any changes, such as fixing erroneous entries, we need to have a good record of such changes. With these goals in mind, we discuss best practices for tracking data provenance. While such practices generally take time and effort to implement, making them seem tedious in the short term, over time, your research will become more reliable, and you and your collaborators will be grateful.

Online platforms such as Amazon’s Mechanical Turk (MTurk), CloudResearch, and Prolific have become a common source of data for behavioral researchers and consumer psychologists alike. This chapter reviews contemporary issues associated with online panel research, discussing first how the COVID-19 pandemic impacted the extent to which researchers use online panels and the workers participating on certain online panels. The chapter explores how factors like a TikTok video can impact who uses these online panels and why. A longitudinal study of researcher perceptions and data quality practices finds that many practices do not align with current recommendations. The authors provide several recommendations for researchers to conduct high-quality behavioral research online, including the use of appropriate prescreens before data collection, data analysis preregistration practices, and avoiding post-screens after data collection that are not preregistered. Finally, the authors recommend researchers thoroughly report details on recruitment, restrictions, completion rates, and any differences in dropout rates across conditions.

Computational information processing has gradually supplanted traditional records and recordkeeping for the physical record, undermining practices centered on the “moral defense” of the record and supplanting them with practices centred on datafication. Prioritizing data malleability rather than the defense of information from manipulation and corruption has, this chapter argues, contributed to the current diminution of the trustworthiness of information and an unravelling of society’s evidentiary foundations. Fields such as archival science and the law have long considered questions of how records may testify to the events and actions of which they form a part – serving as proofs of claims, that is, as evidentials – but research in the field of computing has only relatively recently focused on these issues. Despite its roots in computing culture, blockchain technology offers the promise of an immutable ledger that may halt the processes of datafication contributing to the current widespread potential for manipulation of records. The design and spirit of blockchains – offering the ability to cryptographically “fix” the record, chaining it in place so that any tampering is extremely difficult and immediately evident – harks back to a pre-digital past when the materiality of paper records more readily fixed in place transactional “facts” and protected their integrity from manipulation.

Smart grids (SGs) promise to deliver dramatic improvements compared to traditional power grids thanks primarily to the large amount of data being exchanged and processed within the grid, which enables the grid to be monitored more accurately and at a much faster pace. The smart meter (SM) is one of the key devices that enable the SG concept by monitoring a household’s electricity consumption and reporting it to the utility provider (UP), i.e., the entity that sells energy to customers, or to the distribution system operator (DSO), i.e., the entity that operates and manages the grid. However, the very availability of rich and high-frequency household electricity consumption data, which enables a very efficient power grid management, also opens up unprecedented challenges on data security and privacy. To counter these threats, it is necessary to develop techniques that keep SM data private, and, for this reason, SM privacy has become a very active research area. The aim of this chapter is to provide an overview of the most significant privacy-preserving techniques for SM data, highlighting their main benefits and disadvantages.

Data injection attacks serve as the hallmark example of the security concerns posed by the incorporation of advanced sensing and communication capabilities in power systems. Data injection attacks arise when one or several malicious attackers compromise a subset of the meters used by the state estimation procedure with the aim of manipulating the estimate obtained by the network operator. This chapter surveys the main data injection attacks that are formulated under the assumption that the state variables do not posses a probabilistic description and, therefore, the network operator implements unbiased state estimation procedures. Data injection attacks without this assumption are also studied. In particular, when the network operator perform minimum mean square error (MMSE) estimation, a fundamental trade-off is established between the distortion induced by the attacker and the achievable probability of attack detection. Within this setting, optimal attack strategies are described. The chapter also describes stealth attack constructions that simultaneously minimize the amount of information obtained by the network operator and the probability of attack detection.