Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS), 308-318.

Type

Conference paper

Intellectual base

Primary research

Year

2016

DOI

10.1145/2976749.2978318

Link

https://doi.org/10.1145/2976749.2978318

Cited from

computer_science

Cited by

1 citation across 1 artifact.

Each citation links to the sentence it supports in the citing article.

Primes

• Hidden Information Reconstruction
  • The role mappings are regular and substrate-spanning: the protected input maps to a key, a training record, a redacted word, a tissue density, a private intention; the output channel maps to a power trace, an encrypted packet, a vector embedding, a shared gradient, a CT projection; the prior maps to natural-image statistics, corpus statistics, physical models, expected-text distributions; the defence maps to DP-SGD, padding, decoy traffic, synthetic cohorts.

    This sourceIntroduces DP-SGD, moving differential-privacy noise into ML training as the channel-narrowing defence against gradient/membership leakage.

Verification

This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.

Registry ID ref:1cb32ad2ce79 · see in the full table