DP-GPL: Differentially Private Graph Prompt Learning
Graph Prompt Learning; Membership Inference Attack; Differential Privacy
Yes
No ethics review needed.
1. As mentioned in the first and three point of weaknesses, it would be great the authors could specifically explain the definition of graph DP used in the paper. On the other hand, the authors are also welcomed to discuss the facet mentioned in the third point. Particularly, what would a user be faced with if his/her data possesses high centrality score but requested a low or high privacy level in the individual DP settings?

2. Similar to the reason raised in second point, it would be great if the authors could cook up a specific proof tailored to DP-GPL+W as there are steps concerning private data being added into the original PATE pipeline. 

3. Regarding the description of [1] in Section 2.5, I believe that the setting of label mapping is natural in image domain the fact that model reprogramming (MR) / visual prompting (VP) aims to re-use existing foundational models for either domain transfer or further fine-tuning. In that way, this doesn't seem to be a comparable advantage over GPL as it is simply different. I would suggest the authors to revise the part in the paper. 




[1]: Yizhe Li, Yu-Lin Tsai, Chia-Mu Yu, Pin-Yu Chen, and Xuebin Ren. Exploring the benefits of visual prompting in differential privacy. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5158–5167, 2023.
1. This paper is one of the few papers that investigate the privacy leakage problem in the setting of graph prompt learning. Besides measuring the privacy leakage via MIA to infer the problem of privatized graph prompt learning, the paper also proposes methods to properly alleviate such issues.
2. The experiments is extensive as it covers various datasets along with pre-training methods, rendering the evaluation results holistic.
The paper probes into the problem of privacy concerning graph prompt learning. The paper first demonstrates the the privacy leakage caused by graph prompt learning via MIA. Secondly, in order to resolve the privacy concern, the paper proposed DP-GPL along with its variant version DP-GPL+W which utilizes the well-known PATE structure for differential private guarantees. The experimental results illustrate that the proposed method can preserve the utility of downstream task while maintaining the privacy of the given data.
1. The differential privacy guarantee mentioned in the paper only concerns DP and RDP, which is particularly set for traditional database and datasets that are i.i.d., i.e. with no correlations. It would be great if the authors could further clarify the DP settings used in the paper, e.g., node or edge differential privacy are considered. 

2. Furthermore, the partition scenario that utilize centrality score in DP-GPL+W doesn't seem to be satisfying the partition scenario in the original PATE paper [1]. Particularly, the partition seems to have utilize the private data information without paying any budget in advance which seems to have broken the standard DP guarantee. Although the original PATE allows the partitions to be formed in a non-randomly, it adds to the description that it has to be *naturally and non-randomly* distributed. Namely, the partition could be non-randomly formed by either privacy or regulation concerns that are *a priori* to the actual execution of the training algorithms.

3. Lastly, as DP-GPL+W utilizes the heterogenous definition of DP which are mentioned in [2, 3], the application scenario should be elaborated more on the use case of DP-GPL+W. Particularly, individualized DP refers to privacy standards that could cope with different levels of privacy that are requested by users, i.e., the data holders. However, it seems that in DP-GPL+W, this standard is brought up simply due to the need of weighting mechanism in DP-GPL+W that concerns the graph data itself instead of the different levels of privacy each user or node requested. Does that implies that nodes with higher centrality score are naturally giving out more information in DP-GPL+W regardless of the users' request in privacy levels? Also, how does this associates with the node or edge definition privacy mentioned in the first point?


[1]: Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar. Semisupervised knowledge transfer for deep learning from private training data. In Proceedings of the 5th International Conference on Learning Representations (ICLR), 2017.\
[2]: Boenisch, Franziska, et al. "Individualized PATE: Differentially private machine learning with individual privacy guarantees." arXiv preprint arXiv:2202.10517 (2022).\
[3]:Alaggan, Mohammad, Sébastien Gambs, and Anne-Marie Kermarrec. "Heterogeneous differential privacy." arXiv preprint arXiv:1504.06998 (2015).
Yes
No ethics review needed.
Should it be better to bring algorithm 3 into the main body of this paper?
For the MIA experiment, LiRA attach originally reports true positive at low false positive, which is more sensible than the AUC metric. Have the author considered this?
1. It is the first to show that private information can leak from graph prompts, in particular when
the prompts are tuned over a small number of data points.
2. It shows that naively integrating the DP-SGD algorithms into graph prompt learning yields
impractical privacy-utility trade-offs.
3. It proposes DP-GPL and DP-GPL+W, two algorithms based on the PATE framework
to implement differential privacy guarantees into graph prompt learning.
4. Their new methods achieve both
high utility and strong privacy protections over various setups.
The paper investigates privacy risks in graph prompts by instantiating a membership inference attack that reveals significant
privacy leakage. Then they find that the standard privacy method, DP-SGD, fails to
provide practical privacy-utility trade-offs in graph prompt learning, likely due to
the small number of sensitive data points used to learn the prompts. As a solution,
they propose two algorithms, DP-GPL and DP-GPL+W, for differentially private
graph prompt learning based on the PATE framework, that generate a graph prompt
with differential privacy guarantees.
The privacy mechanism seems like is a direction adaptation from PATE. I believe at least the core part (voting and then adding noise) is very similar to PATE. The other part, despite being claimed by the author to be different from PATE, I do not find it interesting because this is GNN task, which is of-course different from the original task of PATE.
Yes
No ethics review needed.
1. How effective are DP-GPL and DP-GPL+W in resisting MIA? Could the author conduct and report MIA experiments on the DP-protected models (DP-GPL and DP-GPL+W) and compare the results to the unprotected baselines?
2. In DP-GPL+W, the teacher voting is weighted based on node centrality, and the partitioning strategy changes to outperform DP-GPL. Why is this effective? Could the author provide experimental or theoretical evidence, such as ablation studies?
3. Is the proposed method only applicable to homogeneous graphs, or is it suitable for heterogeneous graphs as well?
4. The current experiments focus only on the k-shot scenario where k=5. Could performance comparisons for smaller k-values be added?
5. According to weakness 2, please clarify how your method addresses label privacy.
1. The paper is well-structured, easy to read, and well-written.
2. It addresses privacy risks in graph prompt learning and proposes two non-gradient-based methods.
The paper combines the PATE framework with differential privacy to provide robust privacy protection in graph prompt learning while maintaining high utility. The topic is new and interesting.
1. Lack of Experimentation: In Section 3, the author demonstrates through MIA experiments that prompts based on private graphs can lead to node privacy leakage, proving the existence of privacy risks and subsequently proposing a DP-based protection method. However, while DP provides theoretical guarantees, it is unclear whether there is any impact on the effectiveness of attacks after DP is applied. The current experiment section lacks a demonstration of MIA attack results after DP has been implemented.
2. Unclear Theoretical Section: Graph DP is generally divided into node-DP and edge-DP. The target of the author’s attack experiments is to determine whether a certain node is in the prompt's training graph dataset, which falls under node-DP. However, node-DP involves protecting node features, edges, and labels. In the proposed method, there does not appear to be any protection for labels.
Yes
No ethics review needed.
* Could you please explain l.15 in Algorithm 3 and the blue lines in Fig. 1? That is, how can prompts trained on the private graph be applied to a separate public node/graph? Existing GPL literature (e.g. GPPT) seems to focus on prompting/transforming one specific downstream graph, rather than finding prompts that generalize across graphs.
* How are Cora / Citeseer / Pubmed split into private and public data?
* Is the private part of these graphs used during inference on the public part of the graph?

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Since there is no other field for general comments, here are a few more suggestions for future revisions (**There is no need to respond to these comments**):

* As can be inferred from my comments above, future revisions would benefit from focusing more on the aspects that are unique to graph prompt learning (as opposed to language / vision / ... -prompt learning)
* One way of doing this would be by focusing on adjacency changes / node insertions
* Another way of doing this could be looking into how message-passing interacts with graph prompting (i.e. how is private information propagated through the graph when training a prompt)
* You might also want to consider the effect of message passing when prompting at inference time (unless the private and public part of the graph are kept disjoint)
* I would suggest highlighting best/second-best accuracies in all tables to improve readability.
* The proposed framework is applicable to arbitrary graph prompt learning methods, allowing it to directly benefit from future advances (I would encourage the authors to put more emphasis on this strength in their manuscript).
* First conducting a membership inference attack serves as a convincing motivation for why the considered problem should be worth studying.
* The authors do a good job in contrasting their method to existing DP prompt-learning methods outside the graph domain (including methods inspired by PATE).
* Increasing the utility of PATE via data-dependent ensemble weights is a novel approach that could be of interest for a larger audience.
* The results convincingly demonstrate that the student prompt can achieve comparable utility to the teacher ensemble while offering formal privacy guarantees.
* Results are presented with standard deviations / error bars.
* The general writing style is clear and concise.
* The authors provide an implementation for reproducibility.
The objective of this paper is to develop a differentially private (DP) form of graph prompt learning, i.e., adapting a pre-trained graph neural network to a new ("downstream") graph dataset by learning a transformation ("prompt") of the downstream graph, rather than fine-tuning the model's parameters.

To demonstrate the importance of achieving this objective, the authors first showcase the effectiveness of a standard membership inference attack method on graph prompt methods.

As a provable defense against this and other forms of privacy leakage, the authors then propose a method inspired by the well-known PATE framework, in which an ensemble of teacher *models* is first trained on a disjoint partition of sensitive *dataset*, and then used to generate labels for training of a student *model* on public data in a differentially private manner.
Specifically, the authors' method ("DP-DGL") trains an ensemble of teacher *prompts* on a disjoint partition of a sensitive *graph* and then uses it to generate labels for training of a student *prompt* on a public graph. The authors further propose a graph-specific procedure for partitioning the sensitive data and weighting the ensemble based on node centrality ("DP-DGL+W").

They finally evaluate the privacy-accuracy tradeoff of their two methods on three standard citation graph datasets (Cora, CiteSeer, PubMed), comparing their method to (1) DP-SGD prompt learning, (2) direct application of a pre-trained GNN (3), direct use of the teacher prompt ensemble, and (4) direct use of the best-performing prompt from the  ensemble.
* The manuscript does not specify which neighboring relation / notion of graph privacy is considered. Existing literature on graph DP primarily focuses on either *edge-level* or *node-level* privacy, i.e., preventing privacy leakage due to adjacency changes (see Table 1 in [1]). This adjacency-dependency is the unique challenge that differentiates DP in graphs from DP in other domains. 
* The DPG-DGL-W+ procedure proposed in this paper is neither edge-level nor node-level private. Specifically, the partitioning/weighting procedure (Algorithm 2) depends on centrality scores, which depend on the graph adjacency. Since no steps are undertaken to limit its sensitivity and introduce randomness, the leaked adjacency information may be arbitrarily large ($\epsilon=\infty$).
* Due to the previous point, novelty is severely limited. As stated by the authors, the main differentiator from the DP prompt learning method of Duan et al. (2023) [2] is precisely this problematic partitioning/reweighting procedure (see ll.183-187 and compare Fig. 1 from this manuscript to Fig. 1b from [2]).
* Aside from the conceptually interesting idea of data-dependent ensemble weighting, the work introduces no new methods or theoretic results. As such, it will likely be of limited interest for readers outside the "trustworthy graph prompt learning" sub-niche within the (growing and important) "trustworthy graph ML" niche.
* The manuscript does not provide sufficient background information to follow it without consulting prior work. Specifically, the general paradigm of graph prompt learning is only vaguely and indirectly explained by summarizing related work in Section 2.2. I would strongly suggest introducing a "background" section that defines a generic graph prompt learning procedure and explains the involved terminology (how are GNNs "queried", what is a "target prompt", what is a "shot" in the graph context, ...?)
* The related work section does not discuss prior work on DP for graphs, such as DP graph analysis [3], specialized DP-SGD for graphs [4], specialized DP-GNN architectures [5], or PATE for graphs [6] (see [1] for a survey).

**Minor weaknesses**
* The results only specify $\epsilon$, but not $\delta$ of the $(\epsilon,\delta)$-DP prompts (e.g. Table 1).
* The reported accuracies in Tables 1,5,7-11, even without DP guarantees ($\epsilon=\infty$), are very low. One could probably achieve much higher accuracy by discarding the private data and performing standard semi-supervised learning on the public subgraph of Cora/Citeseer/PubMed. This calls into question why one would want to use prompt learning in the first place.
* While the paper demonstrates that membership inference attacks against graph prompt learning are effective, it does not explain why these should be of any concern. A motivating real-life example (e.g. leaking of communication patterns) might be helpful.
* The authors appear to be using the original DP-SGD w/ moments accounting technique of Abadi et al. (2016) as a "naive solution" baseline. Moments accounting is an obsolescent method that can greatly overestimate privacy leakage due to lossy conversion from Renyi-DP to approximate DP. Using a tight numeric accounting method (e.g. [8]) might yield stronger privacy guarantees and invalidate the findings.
* The "naive solution" appears to ignore the privacy leakage caused by message passing in GNNs (see, e.g., [4]) (Taking this into account would actually be beneficial for the authors, since standard DP-SGD underestimates privacy leakage in MPNNs). 
* The privacy analysis of DP-GPL+W omits one final step (see ll. 385-387). The overall privacy guarantee should be $(\max\{\epsilon_1,\dots,\epsilon_N\}, \delta)$-DP, because the definition of DP involves worst-case pairs of datasets (i.e., we have to make the pessimistic assumption that the modified data is used to train the least private teacher). 
* The pseudo-code in Algorithm 3 is not sufficiently specific about how exactly prompts are trained / used, which makes it hard to reproduce the results from the paper alone. The authors do however provide an implementation in the supplementary material.

[1] Müller et al. Differential Privacy Guarantees for Analytics and Machine Learning on Graphs: A survey of Results. Journal of Privacy and Confidentiality Vol. 14  2023  
[2] Duan et al. Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS 2023  
[3] Kasiviswanathan et al. Analyzing Graphs with Node Differential Privacy. Theory of Cryptography 2013.  
[4] Daigavane et al. Node-Level Differentially Private Graph Neural Networks. arXiv:2111.15521   
[5] Sajadmanesh et al. GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation. USENIX 2023  
[6] Olatunji. Releasing Graph Neural Networks with Differential Privacy Guarantees. TMLR 06/2023.  
[7] Abadi et al. Deep Learning with Differential Privacy. CCS16.  
[8] Google DP Team. Privacy Loss Distributions. https://raw.githubusercontent.com/google/differential-privacy/main/common_docs/Privacy_Loss_Distributions.pdf

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Given these weaknesses, specifically the non-privacy of the proposed DP-GPL-W+ procedure, and the lack of novelty of DP-GPL compared to (Duan et al., 2023) [2], **I recommend rejection of the manuscript**.
I do however believe that private graph prompt learning could be of interest to the trustworthy graph ML community, and would encourage the authors to submit an expanded revision to future conferences (see also suggestions below).
@inproceedings{
anonymous2024dpgpl,
title={{DP}-{GPL}: Differentially Private Graph Prompt Learning},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=dSQtMx6dPE},
note={under review}
}
Graph Neural Networks (GNNs) have shown remarkable performance in various applications. Recently, graph prompt learning has emerged as a powerful GNN training paradigm, inspired by advances in language and vision models. Here, a GNN is pre-trained on public data and then adapted to sensitive tasks using lightweight graph prompts. However, using prompts from sensitive data poses privacy risks.
In this work, we are the first to investigate these risks in graph prompts by instantiating a membership inference attack that reveals significant privacy leakage. We also find that the standard privacy method, DP-SGD, fails to provide practical privacy-utility trade-offs in graph prompt learning, likely due to the small number of sensitive data points used to learn the prompts.
As a solution, we propose two algorithms, DP-GPL and DP-GPL+W, for differentially private graph prompt learning based on the PATE framework, that generate a graph prompt with differential privacy guarantees.
Our evaluation across various graph prompt learning methods, GNN architectures, and pre-training strategies demonstrates that our algorithms achieve high utility at strong privacy, effectively mitigating privacy concerns while preserving the powerful capabilities of prompted GNNs.
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Graph Prompt Learning; Membership Inference Attack; Differential Privacy
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DP-GPL: Differentially Private Graph Prompt Learning
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