8. Privacy, Surveillance, and Data Ethics

Big Data and Privacy; Public vs. Private; Urban Surveillance and Smart Cities; Data Collection and Consent; Cloud Computing; Data Ownership and Open-Source Solutions

As our lives become increasingly intertwined with digital technologies, the boundaries between public and private spheres have grown more complex. The preceding chapters have explored how technology shapes our identities, relationships, and access to opportunities, while also highlighting the responsibilities of both developers and consumers in navigating ethical challenges. From the responsibilities of tech users and professionals, to the impact of technology on justice, equity, and personal well-being, we have seen that ethical decision-making is rarely straightforward – often requiring us to balance competing values and anticipate unintended consequences.

Building on these foundations, this chapter delves into the critical issues of privacy, surveillance, and data ethics. In a world driven by big data, cloud computing, and ubiquitous connectivity, questions about who owns our information, how it is collected, and for what purposes it is used have become central to the ethical landscape. We will examine the evolving definitions of privacy in the digital age, the rise of urban surveillance and smart cities, and the ethical dilemmas posed by large-scale data collection and consent. By considering the implications of data ownership and the responsibilities of both individuals and organizations, this chapter aims to equip readers with the tools to critically assess the ethical dimensions of privacy and surveillance in contemporary society.

Big Data and Privacy

The rise of big data has fundamentally transformed the landscape of personal privacy. Every day, individuals generate vast amounts of digital information through online interactions, purchases, social media activity, and even passive data collection via mobile devices and smart home technology. This information is not only collected by the platforms and services individuals use directly, but is also routinely shared with third-party data aggregators, sold to marketers, and analyzed by a wide array of organizations seeking to infer deeper insights about users’ behaviors and preferences. The sheer scale and interconnectedness of data collection means that a single piece of personal information – such as an email address or geolocation – can be replicated, cross-referenced, and stored in dozens, if not hundreds, of separate databases worldwide.

A crude (but conservative) estimate suggests that for any active digital user, there may be hundreds to thousands of copies of their personal data distributed across various entities. Each online service, retailer, social media platform, and app may create its own record; data brokers and aggregators further multiply these records as they buy, sell, and combine data sets; and backup systems, cloud storage, and analytics platforms add further redundancy. Moreover, big data analytics can infer additional attributes and connections, effectively creating new “copies” of information by extrapolating from existing data points. This exponential proliferation makes it nearly impossible for individuals to fully track or control the spread of their digital footprints.

Consider this scenario: A big cell phone provider sells anonymized (de-identified) data points to a data aggregator and analysis group which cover a period of one-week for a particular region. The data was requested because a store in this region wants to learn more about the individuals who showed up for their big sale event. The aggregator starts by focusing on all of the cell phones that went to that particular store during the store’s event.

But, they don’t stop there. They then review where the data points go after they left the store to see the other destinations for these devices. They discover that quite a number of phones left the store and went to food establishments. This might suggest that the store should have some ‘snacks’ available during their next sale event. They also discover that a number of the devices went to a competitor’s store… this may be interesting to both the original store as well as to the competitor store.

But then, they follow the phones to their ‘final destinations’ for that day to see where they ended up that night. Then, they repeat this process for each day (not just the sale day) to see what else they can learn about the ‘anonymous’ data points. Just by analyzing where the phones end up for the ‘end-of-the-day’ these data points may likely represent the ‘homes’ of the ‘anonymous data points’. And tracking where the phones go each day, they are likely to discover other patterns as well. So much for anonymized data!

Privacy, in its most common definition, refers to the ability of individuals to control the collection, usage, and distribution of their personal information. It encompasses the right to decide what information is shared, with whom, and for what purposes. In the digital age, however, this expectation is increasingly challenged. The default practices of data collection, the complexity of data flows, and the lack of transparency in how information is shared or sold mean that true control over personal data is often illusory. While privacy remains a foundational value and a legal right in many jurisdictions, the reality is that maintaining a reasonable expectation of privacy online requires significant effort, technical literacy, and often, a willingness to opt out of many modern conveniences. Thus, while the principle of privacy is still widely recognized, its practical realization in the age of big data is fraught with challenges and, for many, may no longer be a fully reasonable expectation without substantial systemic change.

Public vs. Private

In the previous section, we considered the expectation of privacy in the age of big-data. So it seems now we should differentiate between the legal definitions vs. the ethical definitions of the terms ‘Public’ vs. ‘Private’.

The distinction between ‘public’ and ‘private’ is foundational both in legal and ethical discussions, yet the definitions and boundaries can shift depending on context. Legally, ‘public’ typically refers to spaces, actions, or information that are accessible or visible to the general population and where individuals have a reduced expectation of privacy. ‘Private,’ on the other hand, denotes areas, behaviors, or data that are restricted to individuals or select groups, where a higher expectation of privacy is recognized and protected by law.

Ethically, the distinction often hinges on the reasonable expectations of those involved. Consider the scenario of looking through an open window into someone’s home: while the window may be open and the view technically accessible from a public sidewalk, most people would agree that peering inside or, more invasively, taking a photo or video crosses an ethical line. The act transitions from a passive observation in a public space to an active intrusion into someone’s private life, highlighting how context and intent matter.

Similarly, recording audio or video of people inside a grocery store – where there is a general expectation of being in a semi-public space – differs ethically (and sometimes legally) from recording those same people outside on a public sidewalk. The boundaries blur further in places like restaurants, public transportation, or even online forums, where the mix of public accessibility and private interaction complicates the ethical calculus.

Additional examples illustrate these nuances. In a workplace, conversations in a private office are generally considered private, while those in a break room may not be. In digital contexts, posting on a public social media page is typically considered public, but sending a direct message is private – though the technical ability to copy, share, or leak messages challenges this expectation. Even in public spaces, certain activities, such as using a restroom or changing clothes in a locker room, retain strong legal and ethical protections of privacy despite their location.

Ultimately, the legal definitions of public versus private are shaped by statutes and case law, often focusing on the impact of actions on society versus individuals. Ethically, the distinction is more fluid, relying on context, societal norms, and the reasonable expectations of those involved. As technology continues to blur these boundaries – through ubiquitous cameras, data collection, and online sharing – it becomes increasingly important to critically examine not just what is legally permissible, but what is ethically respectful of individuals’ privacy and autonomy.

Urban Surveillance and Smart Cities

Urban surveillance and the development of smart cities have introduced a range of technologies that promise to enhance public safety, improve efficiency, and optimize city services. Traffic cameras, for instance, are widely deployed to monitor intersections, enforce traffic laws, and provide real-time data to manage congestion. These systems can reduce accidents and improve emergency response times by allowing authorities to quickly identify and address incidents. Similarly, vehicle tracking – enabled through license plate readers and various connected sensors – can help locate stolen vehicles, optimize public transportation routes, and even support environmental goals by monitoring emissions and traffic patterns.

However, these same technologies raise significant concerns about privacy and the potential for misuse. Traffic cameras and vehicle tracking systems can be repurposed for mass surveillance, enabling authorities or third parties to monitor individuals’ movements without their knowledge or consent. This persistent observation can erode the sense of urban anonymity and create a chilling effect on personal freedom, as people may change their behaviors if they feel constantly watched. The aggregation of vehicle movement data, when combined with other data sources, can reveal sensitive patterns about individuals’ routines and associations.

Facial recognition technology represents another powerful but controversial tool in the smart city arsenal. On the positive side, it can assist in locating missing persons, identifying suspects in criminal investigations, and enhancing security at large public events. Yet, the deployment of facial recognition in public spaces has sparked intense debate over accuracy, bias, and the risk of wrongful identification. Moreover, the widespread use of facial recognition can enable pervasive government or corporate monitoring, undermining civil liberties and disproportionately impacting marginalized communities.

Other notable examples include smart utility meters and environmental sensors. Smart meters can help residents and city officials monitor and reduce energy and water consumption, contributing to sustainability goals and lowering costs. Environmental sensors, such as those monitoring air quality or flood risks, can provide early warnings and improve public health outcomes. Yet, both technologies collect detailed data about residents’ habits and activities, raising questions about who has access to this information and how it might be used beyond its intended purpose.

Ultimately, while urban surveillance and smart city technologies offer clear benefits – improved safety, efficiency, and sustainability – they also introduce complex ethical challenges. The risk of cyberattacks, unauthorized data sharing, and the erosion of privacy demands robust governance, transparent policies, and meaningful public engagement to ensure that technological progress does not come at the expense of individual rights and community trust.

Data Collection and Consent

The distinction between explicit and implied consent is central to understanding how data is collected and used in the digital environment. Explicit consent requires a clear, affirmative action from the user – such as checking a box, signing a form, or clicking an “I Agree” button – indicating unambiguous agreement to the collection and processing of their data. This type of consent is often accompanied by detailed language in End-User License Agreements (EULAs) or Terms of Service (ToS), specifying what data will be collected, how it will be used, and who it may be shared with. For example, a ToS might state, “We collect your name, email, and usage data to provide and improve our services,” and the company requires the user to actively accept these terms before proceeding.

Implied consent, by contrast, is inferred from a user’s actions or the context in which those actions occur. If a user continues to browse a website after being notified of a cookie policy, or submits a contact form expecting a response, their behavior is interpreted as agreement to certain data practices – even if they have not explicitly acknowledged them. Implied consent is often used for routine or less sensitive data collection, but it is inherently less transparent and can lead to ambiguity or disputes over what the user actually agreed to.

A critical nuance in these agreements is the use of open-ended language regarding data use. For instance, a clause might state, “We may use your data for purposes such as backups or translation to another language.” The phrase “such as” does not restrict the company to only those listed uses; rather, it leaves the door open for additional, unspecified uses of the data. The stated intent behind this language (if it is ever actually stated) is said by the company to provide flexibility for operational needs. However, it can also be used to mask broader data exploitation. For example, data collected for “service improvement” could be repurposed for targeted advertising, profiling, or even sold to third parties – uses not explicitly disclosed in the original agreement – but made legal through the agreement as written.

The concept of intent becomes central here. While a company may claim its intent is benign – such as improving user experience or ensuring data security – the same permissions can be leveraged for more intrusive or profit-driven activities, like behavioral advertising, location tracking, or sharing data with law enforcement or other organizations without further user notification. Other examples include using voice recordings from smart speakers to train AI beyond the stated purpose, or aggregating fitness tracker data for insurance risk assessment, even when the original consent was for personal health monitoring.

Legally, the sufficiency of consent – whether explicit or implied – depends on the jurisdiction and the sensitivity of the data involved. Regulations like the General Data Protection Regulation (GDPR) developed and implemented in the European Union, require explicit, informed consent for most personal data processing, especially for sensitive categories, and place the burden on organizations to demonstrate that valid consent was obtained.

Ethically, the bar is even higher: true consent should be informed, freely given, and revocable, with users fully understanding both the scope and intent of data collection. In practice, however, the complexity of agreements and the opacity of data flows make it difficult to prove that users have genuinely understood or agreed to all possible uses of their data.

Likewise, demonstrating the true intent of a company’s data practices is challenging, as broad or ambiguous language can be exploited for purposes far beyond those originally disclosed. As a result, both proving consent and intent remains a fraught process, highlighting the ongoing need for clearer communication, stronger regulation, and more transparent data practices.