mlsanityData to 7 Oct 2026

On the Privacy Risks of Cell-Based NAS Architectures

arXiv 2209.01688 ↗First posted 4 Sep 2022cs.CR cs.LGcited by 5

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Abstract (arXiv)

Existing studies on neural architecture search (NAS) mainly focus on efficiently and effectively searching for network architectures with better performance. Little progress has been made to systematically understand if the NAS-searched architectures are robust to privacy attacks while abundant work has already shown that human-designed architectures are prone to privacy attacks. In this paper, we fill this gap and systematically measure the privacy risks of NAS architectures. Leveraging the insights from our measurement study, we further explore the cell patterns of cell-based NAS architectures and evaluate how the cell patterns affect the privacy risks of NAS-searched architectures. Through extensive experiments, we shed light on how to design robust NAS architectures against privacy attacks, and also offer a general methodology to understand the hidden correlation between the NAS-searched architectures and other privacy risks.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 67 of its 90 references that are in the map. First posted on arXiv 4 Sep 2022.

Builds on

67 of 90 references.

References · 67

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 0; 2025: 0; 2026: 1.

5 papers.

  1. 2023FACE-AUDITOR: Data Auditing in Facial Recognition Systemscited by 15
  2. 2023ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approachcited by 15
  3. 2023DPMLBench: Holistic Evaluation of Differentially Private Machine Learningcited by 13
  4. 2023ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learningcited by 11
  5. 2026Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agentscited by 0