Privacy Preserving Publishing Of Moving Objects Databases
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Privacy-preserving Publishing of Moving Objects Databases
Moving Objects Databases (MOD) have gained popularity as a subject for research due to the latest developments in the positioning technologies and mobile networking. Analysis of mobility data can be used to discover and deliver knowledge that can enhance public welfare. For instance, a study of traffic patterns and congestion trends can reveal some information that can be used to improve routing and scheduling of public transit vehicles. To enable analysis of mobility data, a MOD must be published. However, publication of MOD can pose a threat to location privacy of users, whose movement is recorded in the database. A user's location at one or more time points can be publicly available prior to the publication of MOD. Based on this public knowledge, an attacker can potentially find the user's entire trajectory and learn his/her positions at other time points, which constitutes privacy breach. This public knowledge is a user's quasi-identifier (QID), i.e. a set of attributes that can uniquely identify the user's trajectory in the published database. We argue that unlike in relational microdata, where all tuples have the same set of quasi-identifiers, in mobility data, the concept of quasi-identifier must be modeled subjectively on an individual basis. In this work, we study the problem of privacy preserving publication of MOD. We conjecture that each Moving Object (MOB) may have a distinct QID. We develop a possible attack model on the published MOD given public knowledge of some or all MOBs. We develop k-anonymity model (based on classical k-anonymity), which ensures that every object is indistinguishable (with respect to its QID) from at least k-1 other objects, and show that this model is impervious to the proposed attack model. We employ space generalization to achieve MOB anonymity. We propose three anonymization algorithms that generate a MOD that satisfies the k-anonymity model, while minimizing the information loss. We conduct several sets of experiments on s.
Introduction to Privacy-Preserving Data Publishing
Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Int
Privacy-Preserving Data Publishing
This book is dedicated to those who have something to hide. It is a book about "privacy preserving data publishing" -- the art of publishing sensitive personal data, collected from a group of individuals, in a form that does not violate their privacy. This problem has numerous and diverse areas of application, including releasing Census data, search logs, medical records, and interactions on a social network. The purpose of this book is to provide a detailed overview of the current state of the art as well as open challenges, focusing particular attention on four key themes: RIGOROUS PRIVACY POLICIES Repeated and highly-publicized attacks on published data have demonstrated that simplistic approaches to data publishing do not work. Significant recent advances have exposed the shortcomings of naive (and not-so-naive) techniques. They have also led to the development of mathematically rigorous definitions of privacy that publishing techniques must satisfy; METRICS FOR DATA UTILITY While it is necessary to enforce stringent privacy policies, it is equally important to ensure that the published version of the data is useful for its intended purpose. The authors provide an overview of diverse approaches to measuring data utility; ENFORCEMENT MECHANISMS This book describes in detail various key data publishing mechanisms that guarantee privacy and utility; EMERGING APPLICATIONS The problem of privacy-preserving data publishing arises in diverse application domains with unique privacy and utility requirements. The authors elaborate on the merits and limitations of existing solutions, based on which we expect to see many advances in years to come.