Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures¶

Fredrikson, M., Jha, S., & Ristenpart, T. (2015). Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS), 1322-1333.

Type: Conference paper
Intellectual base: Primary research
Year: 2015
Pages: 1322-1333
DOI: 10.1145/2810103.2813677
DOI registrant: Association for Computing Machinery
Link: https://doi.org/10.1145/2810103.2813677
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
- Model, embedding, and gradient inversion: prediction outputs reconstruct training-set faces; vector embeddings reconstruct source text; shared gradients in federated learning reconstruct contributing batches at near-pixel fidelity, defeating the "data never leaves the device" framing.
  This sourceReconstructs recognizable training-set faces from a model's prediction confidences — model inversion.
  
  Supported in partVerified against the work's full text
  
  Fredrikson et al. support the first clause only: confidence values from a face-recognition model let an attacker recover recognizable face images of training subjects.
  
  “recover recognizable images of people’s faces given only their name and access to the ML model”
  From the work's full text.
  
  Read by an automated reader; not a human review. How support was checked

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