Personal information functions as a valuable asset in the digital economy which is collected, aggregated, and sold, often without an individual's full awareness or meaningful consent. Many applications and devices use "opt-in by default" settings, extracting more data than users may realize. This lack of transparency makes it difficult to track how and where information is used.
Commonly used advertising technology allows the ease of this collection using methods most people are unlikely to be aware of. In one test, vehicles equipped with simple sensors legally gathered data from nearby mobile devices. This passive collection highlights a vulnerability in the current digital ecosystem and raises critical questions about the extent of this invisible harvesting.
The all or nothing transparency problem
This widespread data collection represents one extreme of the privacy challenge. At the other extreme, early blockchain systems were engineered for decentralization, using absolute transparency as the primary mechanism to establish verifiable trust and data integrity without intermediaries.
While visibility can build trust, it also creates new problems by exposing all user activity and personal data. This all or nothing approach to data has hindered adoption in sectors where confidentiality and data protection is necessary, forcing a choice between utility and privacy.
The impact of constant data exposure
The pervasive nature of data collection has direct consequences. When personal data is broadly accessible, it can influence daily life in subtle and direct ways. This constant monitoring creates a digital footprint that may affect an individual's choices, behaviors, or future opportunities. Understanding the stakes is essential to evaluating the problem.
Barriers to individual data protection
Despite these risks, many individuals do not take active steps to protect their data. For example, in the last Midnight community survey, the research found that 76% of respondents are highly concerned about privacy, yet 20% or fewer tend to read privacy policies or check what data apps collect about them. This is an issue with application design: most users are concerned with their privacy, but the user experience is not designed for rational privacy settings.
The burden of privacy management often falls entirely on the user. Navigating complex settings menus and understanding dense privacy policies requires significant effort and technical literacy. This friction suggests the current "opt-out" data sharing model is inconvenient by design, making privacy a luxury rather than a standard.
Defining rational privacy
A proposed solution is to shift the model from "opt-out" to "default-private." New protocols are being developed to help developers build applications that protect user data by default. One solution is selective disclosure, which proves and reveals only the specific information required or shared by the user for a transaction or verification, while keeping all other sensitive data private.
This technical framework enables rational privacy. Rational privacy is the application of reasonable human judgment, using programmable privacy tools, to determine how data is handled. Applied in real-world contexts, this approach allows for nuanced decisions about transparency versus confidentiality that can be tailored to specific industries, applications, and use cases based on user and developer priorities.
Rational privacy empowers both developers and users. By embedding privacy controls directly into an application's architecture, the burden shifts away from the individual. This approach aims to provide the freedom to interact digitally without compromising on privacy.