mlsanityData to 14 Oct 2026

Towards General-purpose Infrastructure for Protecting Scientific Data Under Study

arXiv 2110.01315 ↗First posted 4 Oct 2021cs.CR cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

The scientific method presents a key challenge to privacy because it requires many samples to support a claim. When samples are commercially valuable or privacy-sensitive enough, their owners have strong reasons to avoid releasing them for scientific study. Privacy techniques seek to mitigate this tension by enforcing limits on one's ability to use studied samples for secondary purposes. Recent work has begun combining these techniques into end-to-end systems for protecting data. In this work, we assemble the first such combination which is sufficient for a privacy-layman to use familiar tools to experiment over private data while the infrastructure automatically prohibits privacy leakage. We support this theoretical system with a prototype within the Syft privacy platform using the PyTorch framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 58 references that are in the map. First posted on arXiv 4 Oct 2021.

Builds on

21 of 58 references.

References · 21

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2019Towards Federated Learning at Scale: System Designcited by 832
  4. 2018LEAF: A Benchmark for Federated Settingscited by 792
  5. 2017Renyi Differential Privacycited by 650
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  7. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 348
  8. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  9. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 272
  10. 2018Subsampled R'enyi Differential Privacy and Analytical Moments Accountantcited by 236
  11. 2016Concentrated Differential Privacycited by 144
  12. 2018A generic framework for privacy preserving deep learningcited by 103
9 more
  1. 2020Individual Privacy Accounting via a Renyi Filtercited by 59
  2. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  3. 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
  4. 2017Towards Practical Differential Privacy for SQL Queriescited by 19
  5. 2018Private Machine Learning in TensorFlow using Secure Computationcited by 15
  6. 2014Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacycited by 3
  7. 2019A Programming Framework for Differential Privacy with Accuracy Concentration Boundscited by 3
  8. 2019Duet: An Expressive Higher-order Language and Linear Type System for Statically Enforcing Differential Privacycited by 2
  9. 2019Fuzzi: A Three-Level Logic for Differential Privacycited by 1

Built on by

Not cited yet.