Introduction

Now that more than 20 years have past since Mark Weiser identified this as the century of Ubiquitous Computing (ubicomp) [4] it seems we are no closer to giving users a handle on what is happening with their data. As Weiser noted,

“The problem, while often couched in terms of privacy, is really one of control. If the computational system is invisible as well as extensive, it becomes hard to know what is controlling what, what is connected to what, where information is flowing, how it is being used, what is broken (as compared to what is working correctly, but not helpfully), and what are the consequences of any given action (including simply walking into a room).”

Public places

In terms of privacy, streets, parks, stores, and museums are public places. In contrast, residences are private places.

What determines whether a given location is public is whether it is open to the public.

Fees don’t make a public place private; a road is public, even if it has tolls. And a museum is public, even if the entrance charge is high.

Ownership is not relevant to determining whether a given location is public or private. A privately-owned museum is a public place. A store is public place, even if the land it sits on is privately owned. On the other side, a residence is private, even if it is owned by the government.

Introduction

The “social revolution” introduced by “classic” Online Social Network (OSN) websites (e.g., Facebook, MySpace, Twitter) and, later, by media sharing websites (e.g., Flickr, Youtube, and Instagram), has lead to the fact that the Web as we were used to know it, is nowadays rapidly evolving to incorporate more and more social aspects. In this Social Web vision, users and their resources are linked together via multiple and different kinds of relationships, crossing the boundaries of the specific services used and their related technologies.

In the last years, users interactions have been usually represented by social graphs, describing the online relationships between individuals, and interest graphs, describing the network of people who share interests, but that do not necessarily know each other personally (e.g., followers in Twitter, bought items in e-commerce websites, searches on the Web).

Nowadays, the strict separation among the above definitions is bound to be outdate. In fact, we can already see “interest graph aspects” in applications based on social graphs (e.g., the possibility for a user in Facebook to receive other users public updates even if they are not in his/her social graph and the user does not know them personally), and “social graph aspects” in interest-based applications (e.g., the possibility for users to restrict their searches and data sharing to particular ‘circles’ in Google+). Moreover, in both cases, not only users are connected between them but, according to the specific context of the social/interest graph, also resources can be involved in relationships with users (and other resources).