Software services often depend on storing or processing users' personal data. To promote responsible handling of this information, modern privacy legislation such as the General Data Protection Regulation and California Consumer Privacy Act impose strict regulation around demonstrable privacy enforcement for personal data. In addition to privacy legislation, increased social emphasis on accountability for privacy policies and individual user preferences has added requirements to many systems. This landscape creates interest in technical mechanisms for privacy compliance. Traditional privacy methods such as encryption or anonymization are important, but not sufficient, to address the more nuanced aspects of privacy regulations and policies such as data use requirements based on purpose, fine-grained personal data control, or obligations. An influx of research in both industry and academia seeks to confront this challenge of policy-based privacy enforcement. However, the interdisciplinary nature of privacy, wide variety of approaches, and common gap between theory and software development makes it difficult to navigate the space. To help, this research project presents a systematization of policy-based privacy enforcement with a focus on practical software mechanisms, implementations and frequently adopted privacy-by-policy design patterns. It considers deriving software requirements from natural language requirements, expressing privacy conditions in privacy languages, managing data access with privacy conditions, restricting data flow for privacy, and leveraging logs and audits. Within these domains, the research project explores common approaches, mechanisms, and methodologies and further describes key insights, gaps, and future directions for policy-based privacy enforcement. 

Current smart home devices understandably prioritize the needs of a primary user/owner, who is also typically the purchaser of the device and a corresponding subscription plan. Yet smart home cameras and other smart devices with microphones, location tracking, and other spatial sensing capabilities invariably impact the privacy of people nearby, such as family, friends, guests, neighbors, and domestic workers. We refer to these affected nearby people as adjacent users (or adjacent subjects) because they may interact with smart devices but do so with relatively little or no direct awareness, consent, access, control, or benefit. Although work in privacy and security has begun to address the privacy needs of adjacent users, there is little design research that has responded with either concrete interventions like proposals or prototypes. We present a novel speculative design of a smart home camera called Arca with a physical camera prototype and a mobile application. A significant insight of our empirical and design research is that the most common issues with smart camera privacy is the interpersonal tensions and conflicts stemming from inadequate disclosure, consent, autonomy, and transparency from primary owners. Whereas traditional privacy/security research often focuses on harms from improper disclosure of personally sensitive information, our research suggests that many adjacent users do not necessarily mind being recorded, they do mind the lack of “communication,” “respect,” and “professionalism” from primary users. Furthermore, our studies reveal that even if our specific privacy modes and access sharing features are not regularly used, they may nonetheless function as mechanisms to facilitate better, more open conversation between primary and adjacent users. We continue our work with the goal to enhance adjacent user privacy and experience with privacy-sensitive camera features and reduce tension between adjacent users and primary users.