Detection and Prevention of Sensitive Data From Data Leak Using Shingling and Rabin Filter

Data leak is a major problem in all the organization of any land. A deliberate risk to institution and private security is the disclosure of secure data in transmission and storage. To check content for exposed sensitive data is the main aim for exposed sensitive data. There are large numbers of data-leak cases but human flaws are one of the main reasons of data leak. This paper proposed a data-leak detection model for preventing accidental and intentional data leak in network. If someone succeed to steal some kind of data and send that data to outsider then data owner has obtain to use two methods to find out guilty employee or leaker. This work suggests use of shingling and rabin filter system performs Data Leak Detection (DLD) and Prevention task. The results show that this approach can be effectively implemented in various organizations; however rigorous testing on various data division of such methods will be required to implement the same in sector of importance like defence and other even in large establishment. Keywords—  Information security; Data leak; network security; privacy

A Survey: Data Leakage Detection Techniques

Data is an important property of various organizations and it is intellectual property of organization. Every organization includes sensitive data as customer information, financial data, data of patient, personal credit card data and other information based on the kinds of management, institute or industry. For the areas like this, leakage of information is the crucial problem that the organization has to face, that poses high cost if information leakage is done. All the more definitely, information leakage is characterize as the intentional exposure of individual or any sort of information to unapproved outsiders. When the important information is goes to unapproved hands or moves towards unauthorized destination. This will prompts the direct and indirect loss of particular industry in terms of cost and time. The information leakage is outcomes in vulnerability or its modification. So information can be protected by the outsider leakages. To solve this issue there must be an efficient and effective system to avoid and protect authorized information. From not so long many methods have been implemented to solve same type of problems that are analyzed here in this survey.  This paper analyzes little latest techniques and proposed novel Sampling algorithm based data leakage detection techniques

A Fuzzy Logic based Privacy Preservation Clustering method for achieving K- Anonymity using EMD in dLink Model

Privacy preservation is the data mining technique which is to be applied on the databases without violating the privacy of individuals. The sensitive attribute can be selected from the numerical data and it can be modified by any data modification technique. After modification, the modified data can be released to any agency. If they can apply data mining techniques such as clustering, classification etc for data analysis, the modified data does not affect the result. In privacy preservation technique, the sensitive data is converted into modified data using S-shaped fuzzy membership function. K-means clustering is applied for both original and modified data to get the clusters. t-closeness requires that the distribution of sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table. Earth Mover Distance (EMD) is used to measure the distance between the two distributions should be no more than a threshold t. Hence privacy is preserved and accuracy of the data is maintained

Estado del arte revisión sistemática de la seguridad orientada a Rest

Trabajo de InvestigaciónSe analizaron los principales tipos de ataques informáticos, se describieron las vulnerabilidades más conocidas y los tipos de seguridad recomendados en los servicios Web REST, se hizo la revisión sistemática de las publicaciones que estudian los tipos de vulnerabilidad de Servicios Web REST y se clasificaron con base a una taxonomía de vulnerabilidades con el fin de detallar su estructura.INTRODUCCIÓN 1. SERVICIOS WEB REST 2. SEGURIDAD WEB 3. VULNERABILIDADES DE LOS SERVICIOS WEB 4. METODOLOGÍA 5. RESULTADOS 6. CONCLUSIONES 7. RECOMENDACIONES REFERENCIASPregradoIngeniero de Sistema

Exploring Data Security Management Strategies for Preventing Data Breaches

Insider threat continues to pose a risk to organizations, and in some cases, the country at large. Data breach events continue to show the insider threat risk has not subsided. This qualitative case study sought to explore the data security management strategies used by database and system administrators to prevent data breaches by malicious insiders. The study population consisted of database administrators and system administrators from a government contracting agency in the northeastern region of the United States. The general systems theory, developed by Von Bertalanffy, was used as the conceptual framework for the research study. The data collection process involved interviewing database and system administrators (n = 8), organizational documents and processes (n = 6), and direct observation of a training meeting (n = 3). By using methodological triangulation and by member checking with interviews and direct observation, efforts were taken to enhance the validity of the findings of this study. Through thematic analysis, 4 major themes emerged from the study: enforcement of organizational security policy through training, use of multifaceted identity and access management techniques, use of security frameworks, and use of strong technical control operations mechanisms. The findings of this study may benefit database and system administrators by enhancing their data security management strategies to prevent data breaches by malicious insiders. Enhanced data security management strategies may contribute to social change by protecting organizational and customer data from malicious insiders that could potentially lead to espionage, identity theft, trade secrets exposure, and cyber extortion