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Related Experiment Video

Updated: Nov 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification.

Blaž Meden1, Žiga Emeršič1, Vitomir Štruc2

  • 1Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

We introduce k-Same-Net, a novel face deidentification method using Generative Neural Networks (GNNs) and k-Anonymity. It generates privacy-preserving synthetic faces while preserving data utility.

Keywords:
face deidentificationgenerative neural networksk-Same algorithm

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Sharing image and video data requires robust privacy protection for personal information.
  • Deidentification techniques conceal individual identities in imagery while retaining data utility.
  • Existing methods may not offer formal privacy guarantees or sufficient utility preservation.

Purpose of the Study:

  • To propose k-Same-Net, a novel face deidentification approach combining Generative Neural Networks (GNNs) and k-Anonymity.
  • To provide formal privacy guarantees for deidentified face data within a closed set of identities.
  • To enable control over synthetic face generation for tailored deidentification.

Main Methods:

  • Developed a GNN capable of generating synthetic surrogate face images.
  • Integrated the GNN with the k-Anonymity mechanism for formal privacy guarantees.
  • Utilized appearance-related parameters to control synthetic image characteristics (e.g., expression, age, gender).

Main Results:

  • Demonstrated the feasibility of k-Same-Net on XM2VTS and CK+ datasets.
  • Evaluated deidentification efficacy against state-of-the-art recognition models and competing techniques.
  • Showcased utility preservation through facial expression recognition experiments.

Conclusions:

  • k-Same-Net offers a viable solution for face deidentification with formal privacy guarantees.
  • The approach effectively balances privacy protection and data utility.
  • k-Same-Net presents desirable characteristics compared to existing face deidentification methods.