Analyzing Privacy Loss in Updates of Natural Language Models
To continuously improve quality and reflect changes in data, machine learning-based services have to regularly re-train and update their core models. In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the data used for training before and after the update. We discuss the privacy implications of our findings, propose mitigation strategies and evaluate their effect.
This paper provides an empirical evaluation of the privacy implications of releasing updated versions of language models. The authors show how access to two sequential snapshots of a trained language model can reveal highly specific information about the content of the data used to update the model, even when that data is in-distribution. The paper contains easy to understand, concrete experiments and results, but seems altogether a little underdeveloped. The methodology is sound, but the synthetic experiments around which much of the paper is based may not be sufficiently novel and give little indication of broader implications. It would have been more convincing if these results replicated with two splits of the same dataset, rather than identical datasets with one augmented by canary tokens. The qualitative evaluation of subject-specific updates is also not sufficiently informative. It would have been useful to define a specific attack and see under what circumstances such an attack would succeed. In the current results, I am not convinced that any of the phrases in Table 3 represent a privacy violation. The differential privacy experiment seems to be missing many details: what dataset was this trained on? Are the accuracy values for the training set or a separate testing set? Other works have shown that it is possible to train a differentially private language model without large sacrifices in accuracy, so it would be helpful to know what differentiates this experiment. I would also note that the motivation, a predictive keyboard, is not a situation in which maximizing accuracy is generally desirable: users tend to find this creepy rather than helpful. This is a nice idea but would benefit from some more polishing and more extensive testing.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Analyzing Privacy Loss in Updates of Natural Language Models
To continuously improve quality and reflect changes in data, machine learning-based services have to regularly re-train and update their core models. In the setting of language models, we show that a comparative analysis of model snapshots before and after an update can reveal a surprising amount of detailed information about the changes in the data used for training before and after the update. We discuss the privacy implications of our findings, propose mitigation strategies and evaluate their effect.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper provides an empirical evaluation of the privacy implications of releasing updated versions of language models. The authors show how access to two sequential snapshots of a trained language model can reveal highly specific information about the content of the data used to update the model, even when that data is in-distribution. The paper contains easy to understand, concrete experiments and results, but seems altogether a little underdeveloped. The methodology is sound, but the synthetic experiments around which much of the paper is based may not be sufficiently novel and give little indication of broader implications. It would have been more convincing if these results replicated with two splits of the same dataset, rather than identical datasets with one augmented by canary tokens. The qualitative evaluation of subject-specific updates is also not sufficiently informative. It would have been useful to define a specific attack and see under what circumstances such an attack would succeed. In the current results, I am not convinced that any of the phrases in Table 3 represent a privacy violation. The differential privacy experiment seems to be missing many details: what dataset was this trained on? Are the accuracy values for the training set or a separate testing set? Other works have shown that it is possible to train a differentially private language model without large sacrifices in accuracy, so it would be helpful to know what differentiates this experiment. I would also note that the motivation, a predictive keyboard, is not a situation in which maximizing accuracy is generally desirable: users tend to find this creepy rather than helpful. This is a nice idea but would benefit from some more polishing and more extensive testing.
assistant
This paper provides an empirical evaluation of the privacy implications of releasing updated versions of language models. The authors show how access to two sequential snapshots of a trained language model can reveal highly specific information about the content of the data used to update the model, even when that data is in-distribution. The paper contains easy to understand, concrete experiments and results, but seems altogether a little underdeveloped. The methodology is sound, but the synthetic experiments around which much of the paper is based may not be sufficiently novel and give little indication of broader implications. It would have been more convincing if these results replicated with two splits of the same dataset, rather than identical datasets with one augmented by canary tokens. The qualitative evaluation of subject-specific updates is also not sufficiently informative. It would have been useful to define a specific attack and see under what circumstances such an attack would succeed. In the current results, I am not convinced that any of the phrases in Table 3 represent a privacy violation. The differential privacy experiment seems to be missing many details: what dataset was this trained on? Are the accuracy values for the training set or a separate testing set? Other works have shown that it is possible to train a differentially private language model without large sacrifices in accuracy, so it would be helpful to know what differentiates this experiment. I would also note that the motivation, a predictive keyboard, is not a situation in which maximizing accuracy is generally desirable: users tend to find this creepy rather than helpful. This is a nice idea but would benefit from some more polishing and more extensive testing.