Differential privacy for neighborhood-based collaborative filtering

As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.

Privacy preserving for tagging recommender systems

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called afolksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, adversaries may re-identify a user and her/his sensitivity information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Lap lace noise to the weight of tags We present extensive experimental results on two real world datasets, Delicious and Bibsonomy. While the personalization algorithmis successful in both cases.

FracSym: automated symbolic computation of Lie symmetries of fractional differential equations

In this paper, we present an algorithm for the systematic calculation of Lie point symmetries for fractional order differential equations (FDEs) using the method as described by Buckwar & Luchko (1998) and Gazizov, Kasatkin & Lukashchuk (2007, 2009, 2011). The method has been generalised here to allow for the determination of symmetries for FDEs with n independent variables and for systems of partial FDEs. The algorithm has been implemented in the new MAPLE package FracSym (Jefferson and Carminati 2013) which uses routines from the MAPLE symmetry packages DESOLVII (Vu, Jefferson and Carminati, 2012) and ASP (Jefferson and Carminati, 2013). We introduce FracSym by investigating the symmetries of a number of FDEs; specific forms of any arbitrary functions, which may extend the symmetry algebras, are also determined. For each of the FDEs discussed, selected invariant solutions are then presented. © 2013 Elsevier B.V. All rights reserved.

New generalized Halanay inequalities with applications to stability of nonlinear non-autonomous time-delay systems

In this paper, a general class of Halanay-type non-autonomous functional differential inequalities is considered. A new concept of stability, namely global generalized exponential stability, is proposed. We first prove some new generalizations of the Halanay inequality. We then derive explicit criteria for global generalized exponential stability of nonlinear non-autonomous time-delay systems based on our new generalized Halanay inequalities. Numerical examples and simulations are provided to illustrate the effectiveness of the obtained results.

Privacy preserving data release for tagging recommender systems

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called a folksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, tagging recommender systems has been confronted with serious privacy concerns because adversaries may re-identify a user and her/his sensitive information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy-preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Laplace noise to the weight of tags. We present extensive experimental results on two real world datasets, De.licio.us and Bibsonomy. While the personalization algorithm is successful in both cases, our results further suggest the private releasing algorithm can successfully retain the utility of the datasets while preserving users' identity.

Privacy-preserving topic model for tagging recommender systems

Tagging recommender systems provide users the freedom to explore tags and obtain recommendations. The releasing and sharing of these tagging datasets will accelerate both commercial and research work on recommender systems. However, releasing the original tagging datasets is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive information from tagging datasets with only a little background information. Recently, several privacy techniques have been proposed to address the problem, but most of these lack a strict privacy notion, and rarely prevent individuals being re-identified from the dataset. This paper proposes a privacy- preserving tag release algorithm, PriTop. This algorithm is designed to satisfy differential privacy, a strict privacy notion with the goal of protecting users in a tagging dataset. The proposed PriTop algorithm includes three privacy-preserving operations: Private topic model generation structures the uncontrolled tags; private weight perturbation adds Laplace noise into the weights to hide the numbers of tags; while private tag selection finally finds the most suitable replacement tags for the original tags, so the exact tags can be hidden. We present extensive experimental results on four real-world datasets, Delicious, MovieLens, Last.fm and BibSonomy. While the recommendation algorithm is successful in all the cases, our results further suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy.

Switch point finding using polynomial regression for fuzzy type reduction algorithms

Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for the design of interval type-2 fuzzy logic systems (IT2FLS). Its iterative nature for finding left and right switch points is its Achilles heel. Despite a decade of research, none of the alternative TR methods offer uncertainty measures equivalent to KM algorithm. This paper takes a data-driven approach to tackle the computational burden of this algorithm while keeping its key features. We propose a regression method to approximate left and right switch points found by KM algorithm. Approximator only uses the firing intervals, rnles centroids, and FLS strnctural features as inputs. Once training is done, it can precisely approximate the left and right switch points through basic vector multiplications. Comprehensive simulation results demonstrate that the approximation accuracy for a wide variety of FLSs is 100%. Flexibility, ease of implementation, and speed are other features of the proposed method.