Organizations are increasingly recognizing the value of data collaboration
for data analytics purposes. Yet, stringent data protection laws prohibit the
direct exchange of raw data. To facilitate data collaboration, federated
Learning (FL) emerges as a viable solution, which enables multiple clients to
collaboratively train a machine learning (ML) model under the supervision of a
central server while ensuring the confidentiality of their raw data. However,
existing studies have unveiled two main risks: (i) the potential for the server
to infer sensitive information from the client's uploaded updates (i.e., model
gradients), compromising client input privacy, and (ii) the risk of malicious
clients uploading malformed updates to poison the FL model, compromising input
integrity. Recent works utilize secure aggregation with zero-knowledge proofs
(ZKP) to guarantee input privacy and integrity in FL. Nevertheless, they suffer
from extremely low efficiency and, thus, are impractical for real deployment.
In this paper, we propose a novel and highly efficient solution RiseFL for
secure and verifiable data collaboration, ensuring input privacy and integrity
simultaneously.Firstly, we devise a probabilistic integrity check method that
significantly reduces the cost of ZKP generation and verification. Secondly, we
design a hybrid commitment scheme to satisfy Byzantine robustness with improved
performance. Thirdly, we theoretically prove the security guarantee of the
proposed solution. Extensive experiments on synthetic and real-world datasets
suggest that our solution is effective and is highly efficient in both client
computation and communication. For instance, RiseFL is up to 28x, 53x and 164x
faster than three state-of-the-art baselines ACORN, RoFL and EIFFeL for the
client computation.

我们提出了一种新颖且高效的解决方案 RiseFL，用于安全和可验证的数据协作，同时确保输入的隐私和完整性。

低成本零知识证明的安全可验证数据协作

Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge  Proofs

When AI agents don't align their actions with human values they may cause
serious harm. One way to solve the value alignment problem is by including a
human operator who monitors all of the agent's actions. Despite the fact, that
this solution guarantees maximal safety, it is very inefficient, since it
requires the human operator to dedicate all of his attention to the agent. In
this paper, we propose a much more efficient solution that allows an operator
to be engaged in other activities without neglecting his monitoring task. In
our approach the AI agent requests permission from the operator only for
critical actions, that is, potentially harmful actions. We introduce the
concept of critical actions with respect to AI safety and discuss how to build
a model that measures action criticality. We also discuss how the operator's
feedback could be used to make the agent smarter.

本文提出了一种更有效的解决 AI 安全中的价值同步问题的解决方案，其方法是利用关键指标来测量动作的重要性，只在关键动作时需要操作者进行干预，操作者在处理其他工作时也能保证安全。