在文献Efficient and Privacy-Preserving Multi-Party Skyline Queries Over Encrypted Data中SMSQ协议的内容

SMSQ是一种旨在实现在多个加密数据拥有者之间进行隐私保护的多方天际线查询的协议。具体来说，该协议允许多个数据拥有者通过加密和解密技术，共同计算出多个加密数据集的天际线查询结果，同时保护每个数据集的隐私。

SMSQ协议的主要步骤包括：

1. 数据拥有者对自己的数据集进行加密，使用不同的密钥。

2. 数据拥有者之间进行通信，共同计算出多个加密数据集的天际线。

3. 计算出的天际线结果被加密并发送给每个数据拥有者。

4. 每个数据拥有者使用自己的密钥解密结果。

通过SMSQ协议，多个数据拥有者可以在不暴露自己数据内容的前提下，共同计算出天际线结果，保证了数据的隐私和安全。同时，该协议也具有高效性，具有较快的计算速度和较低的通信开销。

相关问题

Privacy-Preserving Machine Learning Using Federated Learning and Secure Aggregation

Privacy-preserving machine learning is becoming increasingly important in today's world where data privacy is a major concern. Federated learning and secure aggregation are two techniques that can be used to achieve privacy-preserving machine learning.

Federated learning is a technique where the machine learning model is trained on data that is distributed across multiple devices or servers. In this technique, the model is sent to the devices or servers, and the devices or servers perform the training locally on their own data. The trained model updates are then sent back to a central server, where they are aggregated to create a new version of the model. The key advantage of federated learning is that the data remains on the devices or servers, which helps to protect the privacy of the data.

Secure aggregation is a technique that can be used to protect the privacy of the model updates that are sent to the central server. In this technique, the updates are encrypted before they are sent to the central server. The central server then performs the aggregation operation on the encrypted updates, and the result is sent back to the devices or servers. The devices or servers can then decrypt the result to obtain the updated model.

By combining federated learning and secure aggregation, it is possible to achieve privacy-preserving machine learning. This approach allows for the training of machine learning models on sensitive data while protecting the privacy of the data and the model updates.