Set-values are a natural representation of data that appears in a large number of application areas, ranging from retail sales logs to medical records. Datasets with set-values are sparse multidimensional data that usually contain unique combinations of values. Publishing such datasets poses great dangers to the privacy of the individuals that are associated with the records. Adversaries with partial background knowledge can use it to retrieve the whole record that is associated with an individual.

The aim of this article is to provide an overview of the basic anonymization methods for set-valued data. Anonymization methods allow sanitizing set-valued datasets in such a way that the privacy of the individuals who are associated with the data is guaranteed. The paper describes the basic privacy guaranties that are used in research literature, and briefly presents the state-of-the-art anonymization methods.

In today information society, personal data are collected, exchanged and manipulated at a great speed, challenging the attempt of the individual to manage her/his own information. While it is generally recognised that current technologies potentially reduce the privacy of the individual, when compared to the level of privacy enjoyed in the past, it is as well acknowledged that electronic devices can also be used as means for preserving privacy or may at least implement features that encompass legitimate expectations of privacy.

A good balance instrument to ensure privacy while not impeding the circulation of information is the recourse to anonymity. Qualifying anonymity however is not a simple operation.

Due in part to the widespread adoption of Internet-enabled smartphones with positioning capabilities, users are active, mobile participants in today's Internet. This development motivates a broad variety of Internet services that  provide functionality relevant to the users' locations and, more recently, users' social circles.

Emerging services pose significant threats to user privacy that may hinder the spread of these services. This article gives examples of privacy violations that occur when using such services and identifies key properties of this new setting.

We live in times of unprecedented opportunities of sensing, storing and analyzing micro-data on human activities at extreme detail and resolution level, at society scale. Wireless networks and mobile devices record the traces of our movements. Search engines record the logs of our queries for finding information on the web. Automated payment systems record the tracks of our purchases. Social networking services record our connections to friends, colleagues, collaborators.

Location-aware devices, for example, GSM mobile phones, GPS-enabled PDAs, location sensors, and active RFID tags, have been used extensively in recent years. The use of these devices facilitates new and exciting location-based applications. Due to the wide use of the location-aware devices, a huge collection of spatial-temporal moving object data has been generated. Moving object data can be used for various data analysis purposes. However, publication of these mobility data threatens individuals privacy since these raw trajectory data provides location information that can identify individuals and, potentially, their private information. In this article, we review some existing solutions to privacy preserving publishing of moving object data and we discuss unaddressed issues.

Privacy and... sheep

The standard scenario considered in most data privacy discussions is the one in which data describing people is collected, essentially for free, by an individual (or an organization), who then process that data to obtain knowledge valuable for them. This knowledge often becomes an asset to which a monetary value can be assigned. Exaggerating only slightly, a parallel can be drawn with shearing of the sheep:  the sheep (people) give away, often unknowingly, their woolen fleece (the data describing them). The fleece is then processed by scouring, spinning and weaving (data cleaning, data engineering, model building), and all the participants of the process - except the sheep -reap the profit, either monetary or as value added to their operations. The sheep go on growing more fleece, until the next shearing.

Vehicular ad hoc networks (VANETs) can be expected to improve traffic safety and transportation management in the near future. This is realized by letting vehicles exchange their sensed traffic environment changes with other vehicles. Such exchanges also create privacy concerns since the vehicle-generated reports contain much private information on the vehicle and its driver. In this paper we review the existing solutions to preserve privacy in VANETs and we identify unaddressed issues requiring further study.

Today, we are faced with unprecedented opportunities of sensing and storing detailed data of human activities at a society-wide scale, because most of our activities are mediated by the ICT's. For instance, automated payment systems record the tracks of our purchases, search engines record the logs of our queries for finding information on the web, social networking services record our connections to friends, colleagues and collaborators, wireless networks and mobile devices record the traces of our movements and our communications.