Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
Bibliographic record
Abstract
Abstract While significant research advances have been made in the field of deep reinforcement learning, there have been no concreteadversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms tomembership inference attacks. In such attacking systems, the adversary targets the set of collected input data on which thedeep reinforcement learning algorithm has been trained. To address this gap, we propose an adversarial attack frameworkdesigned for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inferenceattack. In particular, we design a series of experiments to investigate the impact of temporal correlation, which naturally existsin reinforcement learning training data, on the probability of information leakage. Moreover, we compare the performance ofcollective and individual membership attacks against the deep reinforcement learning algorithm. Experimental results showthat the proposed adversarial attack framework is surprisingly effective at inferring data with an accuracy exceeding 84% inindividual and 97% in collective modes in three different continuous control Mujoco tasks, which raises serious privacy concernsin this regard. Finally, we show that the learning state of the reinforcement learning algorithm influences the level of privacybreaches significantly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".