Privacy Rights, Inference, and User Trust in Digital Platform Services

Abstrak

Digital services increasingly translate everyday actions into durable data traces that are collected, linked, and interpreted at scale. This literature-based study clarifies how privacy is redefined when personal information is generated continuously through platforms, devices, and connected infrastructures. Privacy is treated as a set of rights and practical conditions: intelligible notice, meaningful consent, limitation of processing purposes, proportional retention, and accountability for automated inferences. The discussion shows that security and privacy are experienced together by users, because breach narratives, authentication frictions, interface defaults, and recovery procedures shape trust and sharing decisions. Individuals negotiate boundaries through selective disclosure, identity separation, permission management, and post-incident adaptations, yet these practices remain vulnerable to inference, aggregation, and third-party data flows. The study synthesizes conceptual arguments on relational privacy, the temporal problem of persistent records, and the epistemic reach of profiling, then connects them to everyday behavioral trade-offs between convenience and self-protection. It concludes that sustainable protection requires aligning system design with human decision limits, reducing hidden secondary uses, and providing usable mechanisms to review, withdraw, and audit data practices. The article contributes an integrated conceptual map that can guide policy drafting, organizational governance, and privacy-respecting product design. Across sectors such as finance, health, education, and employment, these dynamics raise questions of fairness, autonomy, and due process. By articulating boundary principles and behavioral pathways, the study supports more precise evaluation of digital data practices in real-world use.