1,721,130 research outputs found
A probabilistic approach for disclosure risk assessment in statistical databases
No full textIn this paper, disclosure risk assessment in Statistical Databases is performed by means of a probabilistic approach; in particular, we consider the problem of auditing databases that support statistical sum/count/mean/max/min queries to protect the privacy of sensitive boolean data. We provide both a theoretical framework for evaluating the disclosure risk and a tool for its control and management
A System to Prevent Multi-users and Multi-sessions Attack to Breach Privacy Policies in a Trust-End Filter
No full textEmpirical principles and an industrial case study in retrieving equivalent requirements via natural language processing techniques.
No full textThough very important in software engineering, linking artifacts of the same type (clone detection) or different types (traceability recovery) is extremely tedious, error-prone, and effort-intensive. Past research focused on supporting analysts with techniques based on Natural Language Processing (NLP) to identify candidate links. Because many NLP techniques exist and their performance varies according to context, it is crucial to define and use reliable evaluation procedures. The aim of this paper is to propose a set of seven principles for evaluating the performance of NLP techniques in identifying equivalent requirements. In this paper we conjecture, and verify, that NLP techniques perform on a given dataset according to both ability and the odds of identifying equivalent requirements correctly. For instance, when the odds of identifying equivalent requirements are very high, then it is reasonable to expect that NLP techniques will result in good performance. Our key idea is to measure this random factor of the specific dataset(s) in use and then adjust the observed performance accordingly. To support the application of the principles we report their practical application to a case study that evaluates the performance of a large number of NLP techniques for identifying equivalent requirements in the context of an Italian company in the defense and aerospace domain
Recensione a L. CANFORA, G. ZAGREBELSKY, La maschera democratica dell'oligarchia. Un dialogo, a cura di Geminello Preterossi, Bari, Editori Laterza, 2014, pp. 136.
No full textRecensione a L. CANFORA, G. ZAGREBELSKY, La maschera democratica dell'oligarchia. Un dialogo, a cura di Geminello Preterossi, Bari, Editori Laterza, 2014, pp. 136
An Integrated Environment for Reuse Reengineering C Code
No full textThe paper presents an integrated environment implemented in Prolog for reuse reengineering existing C systems. Different tools developed in the RE2 project are integrated in the environment through sharing a fine-grained representation for C programs, the Combined C Graph (CCG). Different views of a system can be abstracted and visualised from the data-base of Prolog facts implementing its CCG representation. Software metric tools evaluate the reengineering costs, while reengineering operations are expressed as transformation rules and a symbolic executor allows the production of the reusable module's specification
A Design Rationale Based Environment for Cooperative Maintenance
No full textThis paper describes Cooperative Maintenance Conceptual Model (CM2), a conceptual model aimed at supporting software maintenance in a collaborative fashion. The main goal of CM2 is to support the software maintenance process through the acquisition, structuring and distribution of the information concerned with the maintenance process itself. Information is structured as a network of linked comments and concerns both the analysis and design activities (Rationale in the Large) and the implementation of a change (Rationale in the Small). We also present COMANCHE (COoperative MAintenance Network Centered Hypertextual Enviroment), an enviroment which reflects the CM2 ideas and principles
An empirical study of metric-based methods to detect obfuscated code
No full textProtecting data and applications from malware and other forms of malicious code has assumed a great relevance in the current era of pervasive web-based applications. Attackers often use code obfuscation to hide harmful programs from automatic detection. Several researchers have proposed methods to classify an unknown program as malicious or benign; however, little work has been done to identify obfuscated code. A promising approach to detect obfuscated code consists of using a set of metrics, collected by static analysis, to classify a program. In this paper we present an empirical evaluation of three text-based metrics to identify obfuscated code. Our experiment shows that the effectiveness of these metrics depends on the obfuscators: there are cases in which the metrics allow the proliferation of false positives (i.e., misclassification of clear code as obfuscated code), which is bothering but not dangerous, and cases where false negatives (i.e. misclassification of obfuscated as clear code) proliferate, which is definitely more dangerous. Based on our experiment, we propose a combination of these three metrics and show how this combination outperforms the individual metrics
Conditioned Program Slicing
No full textSlicing is a technique to decompose programs based on the analysis of the control and data flow. In the original Weiser's definition, a slice consists of any subset of program statements preserving the behaviour of the original program with respect to a program point and a subset of the program variables (slicing criterion), for any execution path. We present conditioned slicing, a general slicing model based on statement deletion. A conditioned slice consists of a subset of program statements which preserves the behaviour of the original program with respect to a slicing criterion for a given set of execution paths. The set of initial states of the program that characterise these paths is specified in the form of a first order logic formula on the input variables. We also show how slices deriving from other statement deletion based slicing models can be defined as conditioned slices. This is used to formally define a partial ordering relation between slicing models and to build a classification framework
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