Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users' physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users' privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent - economical and ethical - purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category.

Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions.

在不同情况下，个人数据通常以分散方式收集和处理。例如，随着分布式应用程序的出现，一些提供商通常会关联其记录，并向其客户提供个性化服务。收集的数据包括用户的地理位置和室内位置，他们的移动方式以及可以获取用户的身体状况，习惯和兴趣的传感器获取的数据。因此，这可能导致不良后果，例如不请自来的广告，甚至导致歧视和跟踪。为了减轻隐私威胁，出现了几种技术，称为“隐私增强技术”。，PET的简称。一方面，服务提供商保护用户隐私的压力越来越大，导致采用了PET。另一方面，服务提供商已经在个性化服务（例如目标广告和新闻）上建立了他们的业务模型。然后，本文的目的是确定哪些PET有潜力满足通常有分歧的（经济和道德）目的。本文确定了一种分类法，将八类PET分为三类，为了更加清晰起见，它考虑了三类个性化服务。在通过举例说明定义并介绍了PET的主要特征之后，本文指出了哪种PET最适合每个个性化服务类别。

然后，它讨论了一些跨学科的隐私挑战，这些挑战可能会减慢这些技术的采用，即：技术，社会，法律和经济问题。最后，它提供了建议并突出了几个研究方向。