In Differential Privacy, There is Truth: Evaluating PATE with Monte Carlo Adversaries
Bibliographic record
Abstract
The shift from centralized to decentralized machine learning (ML) addresses privacy concerns associated with centralized data collection. A prominent approach for learning from decentralized data is the Private Aggregation of Teacher Ensembles, or PATE, which aggregates the predictions of a collection of teacher models. Aggregation is performed through a noised voting mechanism to reveal a collective prediction for the ensemble while providing differential privacy guarantees for the training data of each teacher model. PATE’s differential privacy guarantees protect only against adversaries that observe a bounded number of predictions. PATE provides virtually no privacy guarantees in the realistic setting where an adversary is allowed to query the system continuously. However, the prospects of such an attack have never been evaluated. We contribute to the first study on the confidentiality and privacy guarantees provided by PATE. We devise and implement an attack using Monte Carlo sampling to recover the votes submitted by participants of the PATE protocol, thus breaking PATE’s confidentiality guarantees. Surprisingly, we also show that our adversary is more successful in recovering voting information when the vote-aggregation mechanism introduces noise with a larger variance. Because differential privacy generally benefits from noise with greater variance, this reveals a tension between achieving confidentiality and differential privacy in collaborative learning settings. Next, we observe that PATE and its myriad variants assume that protocol participants, who contribute model votes, are honest. We evaluate scenarios where they can be corrupted by the attacker, and find that attacks become drastically more potent as the attacker is able to control more participants. Robustly defending against the attacks reported in this paper is non-trivial, and is likely to result in a significantly reduced utility of PATE.
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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 | 0.030 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".