PrivateFetch：隐私保护广告中的可扩展目录交付
PrivateFetch: Scalable Catalog Delivery in Privacy-Preserving Advertising
摘要：
为了保持互联网在使用时免费的可能性，人们的注意力转向了新的解决方案，这些解决方案将允许基于用户偏好等行为信息进行有针对性的广告投放，而不会损害用户隐私。最近，设计这种系统的探索要么采取依赖于语义保证的方法，如$k$ -匿名性-当与替代信息结合时很容易被颠覆，并且没有考虑到甚至这些集群的知识本身也是侵犯隐私的可能性。其他方法通过将所有数据和处理逻辑移动到客户端来提供完全的隐私，但这对于客户端和服务器来说都是非常昂贵的。在这项工作中，我们设计了一个名为PrivateFetch的新框架，用于构建依赖于加密硬度和最佳情况隐私的实用广告交付管道，而不是语法隐私保证或依赖于真实世界的匿名化工具。PrivateFetch利用本地首选项计算，然后是高性能的单服务器专用信息检索（PIR），以确保客户端可以从服务器预取广告内容，而不会向内容提供方泄露其任何固有特征。当考虑一个数据库的$>1,000,000$广告，我们表明，我们可以提供$30$广告的客户端在40秒内，总通信成本为192 KB。我们还证明了PrivateFetch的可行性，运行它的货币成本不到平均广告收入的1%。因此，我们的系统能够基于行为和上下文用户信息为客户端预取广告，然后在典型的浏览会话期间显示它们。此外，虽然我们将PrivateFetch作为私人广告交付进行测试，但我们方法的通用性意味着它也可以用于其他内容类型。
英文摘要：
In order to preserve the possibility of an Internet that is free at the point of use, attention is turning to new solutions that would allow targeted advertisement delivery based on behavioral information such as user preferences, without compromising user privacy. Recently, explorations in devising such systems either take approaches that rely on semantic guarantees like $k$-anonymity -- which can be easily subverted when combining with alternative information, and do not take into account the possibility that even knowledge of such clusters is privacy-invasive in themselves. Other approaches provide full privacy by moving all data and processing logic to clients -- but which is prohibitively expensive for both clients and servers. In this work, we devise a new framework called PrivateFetch for building practical ad-delivery pipelines that rely on cryptographic hardness and best-case privacy, rather than syntactic privacy guarantees or reliance on real-world anonymization tools. PrivateFetch utilizes local computation of preferences followed by high-performance single-server private information retrieval (PIR) to ensure that clients can pre-fetch ad content from servers, without revealing any of their inherent characteristics to the content provider. When considering an database of $>1,000,000$ ads, we show that we can deliver $30$ ads to a client in 40 seconds, with total communication costs of 192KB. We also demonstrate the feasibility of PrivateFetch by showing that the monetary cost of running it is less than 1% of average ad revenue. As such, our system is capable of pre-fetching ads for clients based on behavioral and contextual user information, before displaying them during a typical browsing session. In addition, while we test PrivateFetch as a private ad-delivery, the generality of our approach means that it could also be used for other content types.
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