Protecting Confidential Data through Non-Statistical Methods
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Author
Vilhuber, Lars
Abstract
This chapter will rely on and update previous overviews of how researchers, citizens, and administrators can reliably and securely access confidential data, i.e., data that cannot be simply published as “open data”. I will discuss various legal, technical, and practical ways of securing access to data that is needed for computations. This obviously depends on the type and complexity of the computations but also depends on the who, how, and where access is needed.
Description
OA version of the pre-print (submitted version), as per Taylor and Francis contributor agreement.
Sponsorship
Original content draws on work funded by the Alfred P. Sloan Foundation.
Date Issued
2024-10-09
Publisher
Chapman and Hall/CRC
Keywords
Related Version
The DOI points to the published chapter in the Handbook of Sharing Confidential Data Differential Privacy, Secure Multiparty Computation, and Synthetic Data, Edited By Jörg Drechsler, Daniel Kifer, Jerome Reiter, Aleksandra Slavković.
Related DOI
ISBN
9781003185284
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Type
book chapter
