Provenance Views for Module Privacy

Scientific workflow systems increasingly store provenance information about the module executions used to produce a data item, as well as the parameter settings and intermediate data items passed between module executions. However, authors/owners of workflows may wish to keep some of this information confidential. In particular, a module may be proprietary, and users should not be able to infer its behavior by seeing mappings between all data inputs and outputs. The problem we address in this paper is the following: Given a workflow, abstractly modeled by a relation R, a privacy requirement \Gamma and costs associated with data. The owner of the workflow decides which data (attributes) to hide, and provides the user with a view R' which is the projection of R over attributes which have not been hidden. The goal is to minimize the cost of hidden data while guaranteeing that individual modules are \Gamma -private. We call this the "secureview" problem. We formally define the problem, study its complexity, and offer algorithmic solutions

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Benefits of Location-Based Access Control:A Literature Study

Location-based access control (LBAC) has been suggested as a means to improve IT security. By 'grounding' users and systems to a particular location, \ud attackers supposedly have more difficulty in compromising a system. However, the motivation behind LBAC and its potential benefits have not been investigated thoroughly. To this end, we perform a structured literature review, and examine the goals that LBAC can potentially fulfill, \ud the specific LBAC systems that realize these goals and the context on which LBAC depends. Our paper has four main contributions:\ud first we propose a theoretical framework for LBAC evaluation, based on goals, systems and context. Second, we formulate and apply criteria for evaluating the usefulness of an LBAC system. Third, we identify four usage scenarios for LBAC: open areas and systems, hospitals, enterprises, and finally data centers and military facilities. Fourth, we propose directions for future research:\ud (i) assessing the tradeoffs between location-based, physical and logical access control, (ii) improving the transparency of LBAC decision making, and \ud (iii) formulating design criteria for facilities and working environments for optimal LBAC usage