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Recent privacy laws have strengthened data subjects’ right to access personal data collected by companies. Prior work has found that data exports companies provide consumers in response to Data Subject Access Requests (DSARs) can be overwhelming and hard to understand. To identify directions for improving the user experience of data exports, we conducted an online study in which 33 participants explored their own data from Amazon, Facebook, Google, Spotify, or Uber. Participants articulated questions they hoped to answer using the exports. They also annotated parts of the data they found confusing, creepy, interesting, or surprising. While participants hoped to learn either about their own usage of the platform or how the company collects and uses their personal data, these questions were often left unanswered. Participants’ annotations documented their excitement at finding data records that triggered nostalgia, but also shock about the privacy implications of other data they saw. Having examined their data, many participants hoped to request the company erase some, but not all, of the data. We discuss opportunities for future transparency-enhancing tools and enhanced laws.
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Multi-Regulation Computing: Examining the Legal and Policy Questions That Arise From Secure Multiparty Computation
This work examines privacy laws and regulations that limit disclosure of personal data, and explores whether and how these restrictions apply when participants use cryptographically secure multi-party computation (MPC). By protecting data during use, MPC offers the promise of conducting data science in a way that (in some use cases) meets or even exceeds most people’s conceptions of data privacy. With MPC, it is possible to correlate individual records across multiple datasets without revealing the underlying records, to conduct aggregate analysis across datasets which parties are otherwise unwilling to share for competitive reasons, and to analyze aggregate statistics across datasets which no individual party may lawfully hold. However, most adoptions of MPC to date involve data that is not subject to privacy protection under the law. We posit that a major impediment to the adoption of MPC—on the data that society has deemed most worthy of protection—is the difficulty of mapping this new technology onto the design principles of data privacy laws. While a computer scientist might reasonably believe that transforming any data analysis into its privacy-protective variant using MPC is a clear win, we show in this work that the technological guarantees of MPC do not directly imply compliance with privacy laws. Specifically, a lawyer will likely want to ask several important questions about the pre-conditions that are necessary for MPC to succeed, the risk that data might inadvertently or maliciously be disclosed to someone other than the output party, and what recourse to take if this bad event occurs. We have two goals for this work: explaining why the privacy law questions are nuanced and that the lawyer is correct to proceed cautiously, and providing a framework that lawyers can use to reason systematically about whether and how MPC implicates data privacy laws in the context of a specific use case. Our framework revolves around three questions: a definitional question on whether the encodings still constitute ‘personal data,’ a process question about whether the act of executing MPC constitutes a data disclosure event, and a liability question about what happens if something goes wrong. We conclude by providing advice to regulators and suggestions to early adopters to spur uptake of MPC. It is our hope that this work provides the first step toward a methodology that organizations can use when contemplating the use of MPC.
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- PAR ID:
- 10358600
- Date Published:
- Journal Name:
- 2nd ACM Symposium on Computer Science and Law
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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