Privacy Games: Optimal User-Centric Data Obfuscation

In this paper, we design user-centric obfuscation mechanisms that impose the minimum utility loss for guaranteeing user's privacy. We optimize utility subject to a joint guarantee of differential privacy (indistinguishability) and distortion privacy (inference error). This double shield of protection limits the information leakage through obfuscation mechanism as well as the posterior inference. We show that the privacy achieved through joint differential-distortion mechanisms against optimal attacks is as large as the maximum privacy that can be achieved by either of these mechanisms separately. Their utility cost is also not larger than what either of the differential or distortion mechanisms imposes. We model the optimization problem as a leader-follower game between the designer of obfuscation mechanism and the potential adversary, and design adaptive mechanisms that anticipate and protect against optimal inference algorithms. Thus, the obfuscation mechanism is optimal against any inference algorithm

The Evolution of Loan Rate Stickiness Across the Euro Area

To investigate the banking sector integration across euro area countries in terms of loan interest rate stickiness, we estimate structural loan rate curves for 12 euro area countries using time-varying regressions with stochastic volatility. Our results show that the loan rates are sticky to a policy interest rate in all countries for all loan maturities, the degree of stickiness differs across the countries, and the degree of difference is more prominent for longer loan maturities. For short-term loans, the loan rate stickiness decreases and for intermediate- and long-term loans the loan rate stickiness converge to average levels during the sample periods. Banking integration in the euro area is not yet complete, but the degree of heterogeneity in the loan rate stickiness decreases.banking integration, sticky loan interest rate, Bayesian analysis, time-varying regression, Markov chain Monte Carlo

Intelligent XML Tag Classification Techniques for XML Encryption Improvement

Flexibility, friendliness, and adaptability have been key components to use XML to exchange information across different networks providing the needed common syntax for various messaging systems. However excess usage of XML as a communication medium shed the light on security standards used to protect exchanged messages achieving data confidentiality and privacy. This research presents a novel approach to secure XML messages being used in various systems with efficiency providing high security measures and high performance. system model is based on two major modules, the first to classify XML messages and define which parts of the messages to be secured assigning an importance level for each tag presented in XML message and then using XML encryption standard proposed earlier by W3C [3] to perform a partial encryption on selected parts defined in classification stage. As a result, study aims to improve both the performance of XML encryption process and bulk message handling to achieve data cleansing efficiently

A Point Decision For Partially Identified Auction Models

This paper proposes a decision theoretic method to choose a single reserve price for partially identified auction models, such as Haile and Tamer, 2003, using data on transaction prices from English auctions. The paper employs Gilboa and Schmeidler, 1989 for inference that is robust with respect to the prior over unidentified parameters. It is optimal to interpret the transaction price as the highest value, and maximize the posterior mean of the seller’s revenue. The Monte Carlo study shows substantial gains relative to the average revenues of the Haile and Tamer interval.

Architecture and Implementation of a Trust Model for Pervasive Applications

Collaborative effort to share resources is a significant feature of pervasive computing environments. To achieve secure service discovery and sharing, and to distinguish between malevolent and benevolent entities, trust models must be defined. It is critical to estimate a device\u27s initial trust value because of the transient nature of pervasive smart space; however, most of the prior research work on trust models for pervasive applications used the notion of constant initial trust assignment. In this paper, we design and implement a trust model called DIRT. We categorize services in different security levels and depending on the service requester\u27s context information, we calculate the initial trust value. Our trust value is assigned for each device and for each service. Our overall trust estimation for a service depends on the recommendations of the neighbouring devices, inference from other service-trust values for that device, and direct trust experience. We provide an extensive survey of related work, and we demonstrate the distinguishing features of our proposed model with respect to the existing models. We implement a healthcare-monitoring application and a location-based service prototype over DIRT. We also provide a performance analysis of the model with respect to some of its important characteristics tested in various scenarios

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment