论文笔记:A Privacy-Preserving Multipurpose Watermarking Scheme for Audio Authentication and Protection

一、基本信息

论文题目:《A Privacy-Preserving Multipurpose Watermarking Scheme for Audio Authentication and Protection》

发表时间:2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering

作者及单位:

 

二、摘要

在云计算环境下,既要考虑音频内容的隐私保护,又要考虑版权保护和内容认证。本文提出了一种基于云存储和计算的音频多用途加密水印方案。我们对加密音频进行加密域离散小波变换,得到加密系数。利用扩频技术,将鲁棒水印和脆弱水印嵌入到不同的子带中。然后提出了从加密水印音频中提取鲁棒水印和脆弱水印的算法。通过实验验证了该方案的有效性和可行性。结果表明,该方案具有良好的鲁棒性、脆弱性和篡改定位性能。

 

三、主要工作与内容

1、作为多媒体的重要组成部分,音频在互联网和移动操作系统中扮演着非常重要的角色,智能手机已经成为人们上网最常用的平台之一,因此对音频通信、处理和保护的要求比以往更加迫切。例如,音频在whatsapp、facebook messenger、line、imessage、wechat等移动社交应用中得到了广泛的应用,因此,研究具有隐私保护功能的音频通信和处理是

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.
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