sourcepersonal

Sourcepersonal is a term used in privacy and personalization discourse to describe an approach to tailoring content and services that emphasizes data provenance and user control at the source of information. It refers to systems and practices where personalization logic operates on the user's own device or within trusted environments, and where data remains linked to its origin rather than being centralized for profiling.

Key ideas include user consent, data minimization, verifiable data provenance, on-device computation, and cryptographic assurances that personalization is based on the stated data sources. Implementations emphasize transparency about what data is used, where it resides, and how it may be shared, with users retaining the ability to disable or prune data.

Common technical approaches align with the broader privacy-preserving stack, including on-device learning, federated learning, secure enclaves, and policy-driven data access. Sourcepersonal aims to allow tailored recommendations, search results, and accessibility features without exporting raw personal data to centralized servers.

Benefits include enhanced privacy, reduced risk of data breaches, and greater user trust. Challenges involve computational demands on devices, standards for interoperable provenance, and regulatory considerations. Adoption is still emerging, with most discussions in research, policy, and privacy engineering rather than mature commercial products.

The term arose in privacy and data-ethics debates in the 2020s as part of a shift toward user-centric data governance. It remains a concept rather than a widely adopted standard, used to frame design choices and evaluate novel architectures for personalization.

Related topics include data provenance, on-device learning, privacy-preserving computation, and federated learning.