Tailoring Privacy to Users' Needs

This article discusses how the deployment of personalized systems is affected by users’ privacy concerns and by privacy legislation. It shows that these impacts are substantial and will require a significant enhancement of current systems. Basic requirements can already be met with existing technology. Most privacy laws however also impose demands that call for new technologies that still need to be researched. A central conclusion of the paper is that a uniform solution for privacy demands does not exist since both user preferences and legal stipulations are too heterogeneous. Instead, privacy will have to be dynamically tailored to each individual user’s needs, and to the jurisdiction at both the location of the personalized system and that of the user. 1. Personalization in Online Systems is Beneficial for both Internet Users and Internet Sites Computer systems that take individual characteristics of their current users into account and adapt their behavior accordingly have been empirically shown to benefit users in many domains. Examples for successful application areas of these recently so-called personalized systems include education and training (e.g., [1]), online help for complex PC software (e.g., [2, 3]), dynamic information delivery (e.g., [4]), provision of computer access to people with disabilities (e.g., [5]), and to some extent information retrieval systems (e.g., [6]). Recently, personalized systems have also started to conquer the World Wide Web. Personalization thereby is mostly used for purposes of Customer Relationship Management [7]. The single most important way to provide value to customers is to know them and serve them as individuals. The terms micro marketing and one-to-one marketing are being used to describe this business model [8, 9]. Customers need to feel they have a unique personal relationship with the business. Current adaptation to the user is still relatively simple. Examples include customized content (e.g., personalized finance pages or news collections), customized recommendations or advertisements based on past purchase behavior, customized 1 This research has been supported by grants from NSF to the Center for Research on Information Technology and Organizations (CRITO) at the University of California, Irvine. I would like to thank Josef Fink, Judy Kay and Jörg Schreck for their comments on an earlier version