A conditional role-involved purpose-based access control model

This paper presents a role-involved conditional purpose-based access control (RCPBAC) model, where a purpose is defined as the intension of data accesses or usages. RCPBAC allows users using some data for certain purpose with conditions. The structure of RCPBAC model is defined and investigated. An algorithm is developed to achieve the compliance computation between access purposes (related to data access) and intended purposes (related to data objects) and is illustrated with role-based access control (RBAC) to support RCPBAC. According to this model, more information from data providers can be extracted while at the same time assuring privacy that maximizes the usability of consumers' data. It extends traditional access control models to a further coverage of privacy preserving in data mining environment as RBAC is one of the most popular approach towards access control to achieve database security and available in database management systems. The structure helps enterprises to circulate clear privacy promise, to collect and manage user preferences and consent

Exploring personalized life cycle policies

Ambient Intelligence imposes many challenges in protecting people's privacy. Storing privacy-sensitive data permanently will inevitably result in privacy violations. Limited retention techniques might prove useful in order to limit the risks of unwanted and irreversible disclosure of privacy-sensitive data. To overcome the rigidness of simple limited retention policies, Life-Cycle policies more precisely describe when and how data could be first degraded and finally be destroyed. This allows users themselves to determine an adequate compromise between privacy and data retention. However, implementing and enforcing these policies is a difficult problem. Traditional databases are not designed or optimized for deleting data. In this report, we recall the formerly introduced life cycle policy model and the already developed techniques for handling a single collective policy for all data in a relational database management system. We identify the problems raised by loosening this single policy constraint and propose preliminary techniques for concurrently handling multiple policies in one data store. The main technical consequence for the storage structure is, that when allowing multiple policies, the degradation order of tuples will not always be equal to the insert order anymore. Apart from the technical aspects, we show that personalizing the policies introduces some inference breaches which have to be further investigated. To make such an investigation possible, we introduce a metric for privacy, which enables the possibility to compare the provided amount of privacy with the amount of privacy required by the policy

Rethinking De-Perimeterisation: Problem Analysis And Solutions

For businesses, the traditional security approach is the hard-shell model: an organisation secures all its assets using a fixed security border, trusting the inside, and distrusting the outside. However, as technologies and business processes change, this model looses its attractiveness. In a networked world, “inside” and “outside” can no longer be clearly distinguished. The Jericho Forum - an industry consortium part of the Open Group – coined this process deperimeterisation and suggested an approach aimed at securing data rather than complete systems and infrastructures. We do not question the reality of de-perimeterisation; however, we believe that the existing analysis of the exact problem, as well as the usefulness of the proposed solutions have fallen short: first, there is no linear process of blurring boundaries, in which security mechanisms are placed at lower and lower levels, until they only surround data. To the contrary, we experience a cyclic process of connecting and disconnecting of systems. As conditions change, the basic trade-off between accountability and business opportunities is made (and should be made) every time again. Apart from that, data level security has several limitations to start with, and there is a big potential for solving security problems differently: by rearranging the responsibilities between businesses and individuals. The results of this analysis can be useful for security professionals who need to trade off different security mechanisms for their organisations and their information systems

A Framework for High-Accuracy Privacy-Preserving Mining

To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. In this paper, we present a generalized matrix-theoretic model of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, we demonstrate that (a) the prior techniques differ only in their settings for the model parameters, and (b) through appropriate choice of parameter settings, we can derive new perturbation techniques that provide highly accurate mining results even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the model parameters are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at a very marginal cost in accuracy. While our model is valid for random-perturbation-based privacy-preserving mining in general, we specifically evaluate its utility here with regard to frequent-itemset mining on a variety of real datasets. The experimental results indicate that our mechanisms incur substantially lower identity and support errors as compared to the prior techniques

Implanting Life-Cycle Privacy Policies in a Context Database

Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies

Introducing hippocratic log files for personal privacy control

The rapid growth of the Internet has served to intensify existing privacy concerns of the individual, to the point that privacy is the number one concern amongst Internet users today. Tools exist that can provide users with a choice of anonymity or pseudonymity. However, many Web transactions require the release of personally identifying information, thus rendering such tools infeasible in many instances. Since it is then a given that users are often required to release personal information, which could be recorded, it follows that they require a greater degree of control over the information they release. Hippocratic databases, designed by Agrawal, Kiernan, Srikant, and Xu (2002), aim to give users greater control over information stored in a data- base. Their design was inspired by the medical Hippocratic oath, and makes data privacy protection a fundamental responsibility of the database itself. To achieve the privacy of data, Hippocratic databases are governed by 10 key privacy principles. This dissertation argues, that asides from a few challenges, the 10 prin- ciples of Hippocratic databases can be applied to log ¯les. This argument is supported by presenting a high-level functional view of a Hippocratic log file architecture. This architecture focuses on issues that highlight the con- trol users gain over their personal information that is collected in log files. By presenting a layered view of the aforementioned architecture, it was, fur- thermore, possible to provide greater insight into the major processes that would be at work in a Hippocratic log file implementation. An exploratory prototype served to understand and demonstrate certain of the architectural components of Hippocratic log files. This dissertation, thus, makes a contribution to the ideal of providing users with greater control over their personal information, by proposing the use of Hippocratic logfiles