期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：4
页码：731-734
语种：English
出版社：Ayushmaan Technologies
摘要：Data leakage is defined as the accidental or unintentional distribution of private or sensitive data to an unauthorized entity. Sensitive data in companies and organizations include intellectual property (IP), financial information, patient information, personal credit-card data, and other information depending on the business and the industry. Data leakage poses a serious issue for companies as the number of incidents and the cost to those experiencing them continue to increase. Data leakage is enhanced by the fact that transmitted data (both inbound and outbound), including emails, instant messaging, website forms, and file transfers among others, are largely unregulated and unmonitored on their way to their destinations. Furthermore, in many cases, sensitive data are shared among various stakeholders such as employees working from outside the organization’s premises (e.g., on laptops), business partners, and customers. This increases the risk that confidential information will fall into unauthorized hands. Whether caused by malicious intent or an inadvertent mistake by an insider or outsider, exposure of sensitive information can seriously hurt an organization.The Data Leakage problem can be defined as any unauthorized access of data due to an improper implementation or inadequacy of a technology, process or a policy. The “unauthorized access” described above can be the result of a malicious, intentional, inadvertent data leakage, or a bad business/technology process from an internal or external user. Traditionally, this leakage of data is handled by water marking technique which requires modification of data. If the watermarked copy is found at some unauthorized site then distributor can claim his ownership. To overcome the disadvantages of using watermark [2], data allocation strategies are used to improve the probability of identifying guilty third parties. In this project, we implement and analyze a guilt model that detects the agents using allocation strategies without modifying the original data. The guilty agent is one who leaks a portion of distributed data. The idea is to distribute the data intelligently to agents based on sample data request and explicit data request in order to improve the chance of detecting the guilty agents. The algorithms implemented using fake objects will improve the distributor chance of detecting guilty agents. It is observed that by minimizing the sum objective the chance of detecting guilty agents will increase. We also developed a framework for generating fake objects.
