AccretionLink: On-Device Auditing of Exposure-Control Attacks on Attribute Inference
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Abstract (arXiv)
Exposure control lets an adversary rank authentic public posts to strengthen private-attribute inference without altering content. AccretionLink defines confidentiality and integrity games for this attack, models bounded selection odds through partial identification, and constructs dependence-aware time-uniform e-processes. On 52 held-out synthetic profiles, odds-four selection reduced aggregate negative log likelihood at every horizon. At eight posts the advantage was 0.01595 nats (95% CI [0.00890, 0.02336]), three of four target effects survived Holm adjustment, and label-blind model-guided selection caused 6/109 high-confidence false reversals. On 142 PAN15 test profiles, exploratory selection produced a 0.01227-nat advantage but no reversal. A separate TF-IDF selector retained a 0.01470-nat advantage against the unchanged G5 target, while matched identity shuffling did not reproduce it. Pixel 10 encoded all 1,622 held-out posts once with a fallback-free Tensor G5 graph. A P-256 checkpoint authenticated the selected-replay, actual-model, native-report, and operation digests; local KeyInfo identified the signing key as StrongBox-backed.
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References · 10
- 2017On Calibration of Modern Neural Networkscited by 4,960
- 2023Beyond Memorization: Violating Privacy Via Inference with Large Language Modelscited by 175
- 2020Estimating means of bounded random variables by bettingcited by 169
- 2022Game-theoretic statistics and safe anytime-valid inferencecited by 155
- 2025EmbeddingGemma: Powerful and Lightweight Text Representationscited by 139
- 2018AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learningcited by 60
- 2024A Synthetic Dataset for Personal Attribute Inferencecited by 36
- 2016You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviorscited by 34
- 2025The Eye of Sherlock Holmes: Uncovering User Private Attribute Profiling via Vision-Language Model Agentic Frameworkcited by 9
- 2026What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Mediacited by 2
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