Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: This paper studies how to bring secure computation to social recommendation system, by leveraging the distributed (or sparse) data on different platforms while preserving their data privacy. The authors analysis the conventional recommender algorithm's demand on social terms, and propose two secure sparse matrix multiplication methods to conduct the computation. They compare their proposed method with two classical baselines with regards to accuracy, running time and communication size, showing competitive performance and more efficient computation.
main_review: Strength:
* The paper proposed an efficient approach to social recommendation.

Weakness:
* Experiment. Since the main claim of the paper is its efficiency, in terms of social recommendation, large scale experiments are expected. However the paper only presents results on small scale datasets. Large scale results on datasets such as Amazon produce review are more convincing.
* Recommendation model. As the proposed computation method seems not tied to a specific recommendation model. More results on modern recommendation models will help the argument, such as "Deep Learning Recommendation Model for Personalization and Recommendation Systems" and "Collaborative filtering and deep learning based recommendation system for cold start items".
limitations_and_societal_impact: Yes
review_title: null

final_score: rating: 9: Top 15% of accepted NeurIPS papers, strong accept
review_content: summary: In this paper, the authors focus on improving the efficiency of a specific problem: secure cross-platform social recommendation. They exploit the sparsity of social data by proposing two novel and secure protocols for matrix multiplication. They also provide theoretical results of both the efficiency and security of their protocols. Also, in experiments, the proposed framework --- S^3^Rec improves the communication and computation efficiency of prior work by approximately 40x and 423x.
main_review: Data security and privacy is a hot and important research topic. Build secure recommendation models across domains is of great impact in practice. The idea of exploiting data sparsity in secure cross-platform social recommendation is new. Their proposed secure matrix multiplication protocols are novel to me (in the sense that there seem no existing PIR-based sparse matrix multiplication protocols), and the efficiency improvement brought by those two protocols is quite promising.

This work is technically written with thorough theoretical analysis. In particular, they include detailed security proof. Most of the arguments presented in this work are supported by either solid technical analysis or experiment results. 

This paper is well-organized, and it clearly states the main problem in the introduction section, while some technical details are pretty dense and required patient reading, especially for the readers who are not experts in S&P area.

To the best of my knowledge, this paper is the first work exploiting the data sparsity in secure cross-platform social recommendation, and their experiments show remarkable improvements (in terms of efficiency) over the prior framework. Although the adopted social recommendation model is a little out-of-date, I believe it points out the feasibility and practicability of large-scale secure social recommendation.

However, I do have some questions about this paper:

1. Is it possible to use a similar method to improve efficiency on more complicated secure social recommendation models? For example, use secure sparse matrix multiplication protocol on a NN-based recommendation model.
2. Can we replace PIR with other cryptographic primitives in a secure sparse matrix multiplication protocol?

And there are some typos:

1. line 305, there has been several works -> work
2. line 106, we present three popular MPC protocols -> two

Minor concerns:

The authors could state more applications of their secure sparse matrix multiplication protocols, as it shows the generalization of their proposed protocols. Also, I personally recommend the authors try to apply more complex social recommendation models such as the nn-models. Though it's known to be difficult to implement MPC-NN protocol, the authors can discuss (in theory) the impact of exploiting data sparsity in NN-based social recommendation models.

In summary, I think the problem this paper aims to solve is important, the proposed technical solution is novel, and the experimental results are impressive. I strongly support the acceptance of this paper. 

limitations_and_societal_impact: See main review
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: In this paper, the authors study the problem of social recommendation, i.e., rating prediction with a user-item rating matrix and a user-user social relation matrix. Specifically, the authors propose a novel privacy-aware method, i.e., sparsity-aware secure cross-platform social recommendation (S^3Rec).  The main idea of S^3Rec is to compute the social term in the objective function of a classic social recommendation method (see Eq.(1)) by two different parties (or platforms) in a privacy-aware manner via homomorphic encryption and private information retrieval.
main_review: 1 Originality: 
The proposed method, i.e., sparsity-aware secure cross-platform social recommendation (S^3Rec), is new since no previous work use homomorphic encryption (HE) and private information retrieval (PIR) in such a way for jointly computing the social term in a social recommendation method. Moreover, the difference between the proposed method and the most closely related work, i.e., SeSoRec [5], is also well identified, i.e., security and efficiency.

2 Quality:
The proposed model is technically sound, which is also supported by the empirical studies, i.e., the proposed methods is efficient and accurate.

3 Clarity:
Overall, the paper is well presented and is easy to follow. The motivations on leveraging the social connections for rating prediction in a privacy-aware manner are well justified. Sufficient details of the protocals are also included.

4 Significance: 
The authors have conducted extensive empirical studies, including 2 datasets, as well as studies about the effectiveness of method with different levels of sparsites. For rating prediction, the basic MF model is known not the state-of-the-art. For social recommendation, SoRec is also no the state-of-the-art. Moreover, it is not clear whether the proposed framework S^3Rec can be applied to more advanced social recommendation methods.

limitations_and_societal_impact: Yes.
review_title: null

final_score: rating: 4: Ok but not good enough - rejection
review_content: summary: This paper proposes a secure and efficient cross-platform social recommendation algorithm. It solves a standard matrix factorization problem using network information, in which one platform has the rating data and another has the social information. 

main_review: Strengths: 

The paper is well-written ad easy to follow. 

Comments: 

(1) My first comment on the paper is about the problem setup. I think the paper needs justifications to (1) demonstrate the practicality (how reasonable it is). Specifically, I wonder how reasonable it is to assume that two stakeholders store social and rating information separately. Realistic examples will be helpful to demonstrate the actual value of the problem formulation. I think it is more reasonable to assume that two platforms have the same information on items, e.g., Spotify, youtube, and pandora have information on similar music. 

(2)  Still related to the problem setup, I think there are security issues related to the problem setup. Why and how can two platforms match this sensitive information on user identity? To do so requires the platform to know some identifiable information and that they will be able to communicate through this unique identifier. 

(3) There are other matrix factorization methods, such as svd++ and different deep learning-based approaches. I suspect that even without social information, these methods may be able to achieve better performance. If this is the case, it is not clear this stylized extension on matrix factorization to obtain security is useful or not. I think the experiment section can be enriched by comparing with more benchmarks (without network information).

(4) Regarding the communication mechanism in section 3.1, it is unclear how it can communicate the gradients in the second part of eq (2) without security concern. Specifically, for each iteration, there are no gradients and n(n-1)/2 entries. Suppose there are T iterations in total, when k * T ≥ (n-1)/2, it is possible for P0 to recover social influence in P1. There may be things I miss; please correct me if I am wrong.
limitations_and_societal_impact: None.
review_title: null

Meta-review: The reviews were generally positive about the motivation for the problem, novelty of the techniques, and execution of the paper. They identified a few areas for potential improvement, including expanding the scope of the experiments and handling more state-of-the-art recommendation systems. These concerns were adequately addressed in author feedback. The authors convincingly explained how their techniques would generalize to other methods based on sparse matrix multiplication, and gave an analysis for SVD++ as a specific example. I recommend the paper for acceptance.

My personal enthusiasm for the paper is a touch lower than that of the reviewers because the MPC-based security guarantees of the framework hold at a per-iteration level, rather than giving an end-to-end guarantee for the whole training process. (That is, allowing each party to learn the full sequence of social updates is potentially more disclosive than allowing each party to learn the final model.) This is a weaker guarantee, and less technically interesting to achieve. Nevertheless, the security obtained does still appear to be new and meaningful.

Comment for the authors: Please precisely define the MPC-hybrid and PIR-hybrid with leakage models in which the protocols are proved secure.
Decision: Accept (Poster)