We propose Falcon, an end-to-end 3-party protocol for efficient private
training and inference of large machine learning models. Falcon presents four
main advantages - (i) It is highly expressive with support for high capacity
networks such as VGG16 (ii) it supports batch normalization which is important
for training complex networks such as AlexNet (iii) Falcon guarantees security
with abort against malicious adversaries, assuming an honest majority (iv)
Lastly, Falcon presents new theoretical insights for protocol design that make
it highly efficient and allow it to outperform existing secure deep learning
solutions. Compared to prior art for private inference, we are about 8x faster
than SecureNN (PETS'19) on average and comparable to ABY3 (CCS'18). We are
about 16-200x more communication efficient than either of these. For private
training, we are about 6x faster than SecureNN, 4.4x faster than ABY3 and about
2-60x more communication efficient. Our experiments in the WAN setting show
that over large networks and datasets, compute operations dominate the overall
latency of MPC, as opposed to the communication.

本文提出了一种名为 Falcon 的端到端的 3 方协议，用于高效的私有机器学习模型训练和推理，它支持高效网络并且包容批量归一化，有安全性保证，并且比先前的解决方案更快速和更高效。

FALCON：私有深度学习的诚实大多数恶意安全框架

FALCON: Honest-Majority Maliciously Secure Framework for Private Deep  Learning

Nowadays, privacy preserving machine learning has been drawing much attention
in both industry and academy. Meanwhile, recommender systems have been
extensively adopted by many commercial platforms (e.g. Amazon) and they are
mainly built based on user-item interactions. Besides, social platforms (e.g.
Facebook) have rich resources of user social information. It is well known that
social information, which is rich on social platforms such as Facebook, are
useful to recommender systems. It is anticipated to combine the social
information with the user-item ratings to improve the overall recommendation
performance. Most existing recommendation models are built based on the
assumptions that the social information are available. However, different
platforms are usually reluctant to (or cannot) share their data due to certain
concerns. In this paper, we first propose a SEcure SOcial RECommendation
(SeSoRec) framework which can (1) collaboratively mine knowledge from social
platform to improve the recommendation performance of the rating platform, and
(2) securely keep the raw data of both platforms. We then propose a Secret
Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and
prove its correctness and security theoretically. By applying minibatch
gradient descent, SeSoRec has linear time complexities in terms of both
computation and communication. The comprehensive experimental results on three
real-world datasets demonstrate the effectiveness of our proposed SeSoRec and
SSMM.

本研究提出 SeSoRec 框架，并基于 Secret Sharing based Matrix Multiplication 协议优化其算法。实证结果表明，SeSoRec 对于保护用户数据和提高推荐性能是有效的。

基于秘密分享的安全社交推荐

Secure Social Recommendation based on Secret Sharing

The increasing interest in user privacy is leading to new privacy preserving
machine learning paradigms. In the Federated Learning paradigm, a master
machine learning model is distributed to user clients, the clients use their
locally stored data and model for both inference and calculating model updates.
The model updates are sent back and aggregated on the server to update the
master model then redistributed to the clients. In this paradigm, the user data
never leaves the client, greatly enhancing the user' privacy, in contrast to
the traditional paradigm of collecting, storing and processing user data on a
backend server beyond the user's control. In this paper we introduce, as far as
we are aware, the first federated implementation of a Collaborative Filter. The
federated updates to the model are based on a stochastic gradient approach. As
a classical case study in machine learning, we explore a personalized
recommendation system based on users' implicit feedback and demonstrate the
method's applicability to both the MovieLens and an in-house dataset. Empirical
validation confirms a collaborative filter can be federated without a loss of
accuracy compared to a standard implementation, hence enhancing the user's
privacy in a widely used recommender application while maintaining recommender
performance.

本文介绍了一个基于随机梯度方法的联邦协作过滤模型，并将其应用于用户推荐系统，旨在提高用户隐私保护，且实验表明，在联邦实现中，相较于标准实现而言，推荐系统的性能并未降低。