A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

The importance of software security has been profound, since most attacks to software systems are based on vulnerabilities caused by poorly designed and developed software. Furthermore, the enforcement of security in software systems at the design phase can reduce the high cost and effort associated with the introduction of security during implementation. For this purpose, security patterns that offer security at the architectural level have been proposed in analogy to the well-known design patterns. The main goal of this paper is to perform risk analysis of software systems based on the security patterns that they contain. The first step is to determine to what extent specific security patterns shield from known attacks. This information is fed to a mathematical model based on the fuzzy-set theory and fuzzy fault trees in order to compute the risk for each category of attacks. The whole process has been automated using a methodology that extracts the risk of a software system by reading the class diagram of the system under study.

The identification of design patterns as part of the reengineering process can convey important information to the designer. However, existing pattern detection methodologies generally have problems in dealing with one or more of the following issues: identification of modified pattern versions, search space explosion for large systems and extensibility to novel patterns. In this paper, a design pattern detection methodology is proposed that is based on similarity scoring between graph vertices. Due to the nature of the underlying graph algorithm, this approach has the ability to also recognize patterns that are modified from their standard representation. Moreover, the approach exploits the fact that patterns reside in one or more inheritance hierarchies, reducing the size of the graphs to which the algorithm is applied. Finally, the algorithm does not rely on any pattern-specific heuristic, facilitating the extension to novel design structures. Evaluation on three open-source projects demonstrated the accuracy and the efficiency of the proposed method

Feature matching is a prerequisite to a wide variety of vision tasks. This paper presents a method that addresses the problem of matching disparate views of coplanar points and lines in a unified manner. The proposed method employs a randomized search strategy combined with the two-line two-point projective invariant to derive small sets of possibly matching points and lines. These candidate matches are then verified by recovering the associated plane homography, which is further used to predict more matches. The resulting scheme is capable of successfully matching features extracted from views that differ considerably, even in the presence of large numbers of outlying features. Experimental results from the application of the method to indoor and aerial images indicate its effectiveness and robustness. 1 Introduction A fundamental problem in computer vision, that appears in different forms in tasks such as discrete motion estimation, feature-based stereo, object recognition, image r...