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Organized surveillance, especially by governments poses a major challenge to individual privacy, due to the resources governments have at their disposal, and the possibility of overreach. Given the impact of invasive monitoring, in most democratic countries, government surveillance is, in theory, monitored and subject to public oversight to guard against violations. In practice, there is a difficult fine balance between safeguarding individual’s privacy rights and not diluting the efficacy of national security investigations, as exemplified by reports on government surveillance programs that have caused public controversy, and have been challenged by civil and privacy rights organizations. Surveillance is generally conducted through a mechanism where federal agencies obtain a warrant from a federal or state judge (e.g., the US FISA court, Supreme Court in Canada) to subpoena a company or service-provider (e.g., Google, Microsoft) for their customers’ data. The courts provide annual statistics on the requests (accepted, rejected), while the companies provide annual transparency reports for public auditing. However, in practice, the statistical information provided by the courts and companies is at a very high level, generic, is released after-the-fact, and is inadequate for auditing the operations. Often this is attributed to the lack of scalable mechanisms for reporting and transparent auditing. In this paper, we present SAMPL, a novel auditing framework which leverages cryptographic mechanisms, such as zero knowledge proofs, Pedersen commitments, Merkle trees, and public ledgers to create a scalable mechanism for auditing electronic surveillance processes involving multiple actors. SAMPL is the first framework that can identify the actors (e.g., agencies and companies) that violate the purview of the court orders. We experimentally demonstrate the scalability for SAMPL for handling concurrent monitoring processes without undermining their secrecy and auditability.
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The SCALES Project: Making Federal Court Records Free
Federal court records have been available online for nearly a quarter century, yet they remain frustratingly inaccessible to the public. This is due to two primary barriers: (1) the federal government's prohibitively high fees to access the records at scale and (2) the unwieldy state of the records themselves, which are mostly text documents scattered across numerous systems. Official datasets produced by the judiciary, as well as third-party data collection efforts, are incomplete, inaccurate, and similarly inaccessible to the public. The result is a de facto data blackout that leaves an entire branch of the federal government shielded from empirical scrutiny. In this Essay, we introduce the SCALES project: a new data-gathering and data-organizing initiative to right this wrong. SCALES is an online platform that we built to assemble federal court records, systematically organize them and extract key information, and-most importantly-make them freely available to the public. The database currently covers all federal cases initiated in 2016 and 2017, and we intend to expand this coverage to all years. This Essay explains the shortcomings of existing systems (such as the federal government's PACER platform), how we built SCALES to overcome these inadequacies, and how anyone can use SCALES to empirically analyze the operations of the federal courts. We offer a series of exploratory findings to showcase the depth and breadth of the SCALES platform. Our goal is for SCALES to serve as a public resource where practitioners, policymakers, and scholars can conduct empirical legal research and improve the operations of the federal courts. For more information, visit www.scales-okn.org.
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- PAR ID:
- 10558826
- Publisher / Repository:
- SSRN
- Date Published:
- Journal Name:
- Northwestern University Law Review
- Volume:
- 119
- Issue:
- 1
- ISSN:
- 1556-5068
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2139/ssrn.4948027
