12-01-2015, 07:28 AM
can i get data leakage detection base paper??
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data leakage detection base paper free download
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can i get data leakage detection base paper??
27-03-2017, 11:07 AM
The Data Leak Detection Project proposes data allocation strategies that improve the probability of identifying leaks. In some cases, we can also inject "realistic but false" data records to further improve our chances of detecting leaks and identifying the culprit. In the course of doing business, sometimes sensitive data must be delivered to supposedly trusted third parties. Another company can get out of their data processing, so the data must be given to several other companies. There is always a risk of agent data leaking. Leak detection is handled by watermarks, for example, a unique code is embedded in each distributed copy. If that copy is later discovered by an unauthorized party, the loss can be identified. But again requires code modification. Watermarks can sometimes be destroyed if the recipient of the data is malicious. Traditionally, leak detection is handled by watermarks, for example, a unique code is embedded in each distributed copy. If that copy is later discovered by an unauthorized party, the loss can be identified. Watermarks can be very useful in some cases, but again, they involve a modification of the original data. In addition, watermarks can sometimes be destroyed if the recipient of the data is malicious.
In this work, discrete techniques are studied to detect leaks of a set of objects or registers. Specifically, we study the following scenario: After giving a set of objects to agents, the dealer discovers some of those same objects in an unauthorized place. (For example, data can be found on a website or can be obtained through a legal discovery process). At this point, the distributor can evaluate the probability that the filtered data will come from one or more agents, independently by other means. Using an analogy with cookies stolen from a cookie jar, if we take Freddie with a single cookie, you may argue that a friend gave him the cookie. But if we get Freddie with five cookies, it will be much harder for him to argue that his hands were not in the cookie jar. If the dealer sees "sufficient evidence" that an agent has leaked data, he may stop doing business with it, or may initiate legal proceedings. In this paper, we developed a model to evaluate the "fault" of the agents. Finally, we also consider the option of adding "false" objects to the distributed set. Such objects do not correspond to real entities, but they seem realistic to agents. In a sense, fake objects act as a type of watermark for the whole set, without modifying any individual member. If it turns out that an agent was given one or more false objects that were leaked, then the dealer may be more certain that the agent was guilty.
The application of technology leak detection models data. This work addresses the new research technique for secure data transmission and leak detection, if it is filtered. A data distributor has given confidential data to trusted third parties (agents) .if some data is leaked and found in an unauthorized place. The distributor must analyze that the filtered data came from (where) one or more agents. We propose data allocation strategies to improve the probability of identifying leaks. We can also add "false object" to further improve our chances of detecting leaks and identify the culprit.
Data leakage is the unauthorized transmission of data or information from an organization to an external destination or recipient. Data leakage is defined as the accidental or intentional distribution of confidential or confidential data to an unauthorized entity. Confidential business and organizational data includes intellectual property, financial information, patient information, personal credit card data, and other information that is business and industry dependent. A data distributor has given this confidential data to a set of allegedly trusted agents (third parties). Some of the data is filtered and found in an unauthorized place. The distributor must evaluate the probability that the filtered data comes from one or more agents, rather than having been obtained independently by other means. We call the data owner to the distributor and the allegedly trusted third parties. Our goal is to detect when sensitive data from the distributor has been filtered by agents and, if possible, identify the agent that filtered the data.
In this work, discrete techniques are studied to detect leaks of a set of objects or registers. Specifically, we study the following scenario: After giving a set of objects to agents, the dealer discovers some of those same objects in an unauthorized place. (For example, data can be found on a website or can be obtained through a legal discovery process). At this point, the distributor can evaluate the probability that the filtered data will come from one or more agents, independently by other means. Using an analogy with cookies stolen from a cookie jar, if we take Freddie with a single cookie, you may argue that a friend gave him the cookie. But if we get Freddie with five cookies, it will be much harder for him to argue that his hands were not in the cookie jar. If the dealer sees "sufficient evidence" that an agent has leaked data, he may stop doing business with it, or may initiate legal proceedings. In this paper, we developed a model to evaluate the "fault" of the agents. Finally, we also consider the option of adding "false" objects to the distributed set. Such objects do not correspond to real entities, but they seem realistic to agents. In a sense, fake objects act as a type of watermark for the whole set, without modifying any individual member. If it turns out that an agent was given one or more false objects that were leaked, then the dealer may be more certain that the agent was guilty.
The application of technology leak detection models data. This work addresses the new research technique for secure data transmission and leak detection, if it is filtered. A data distributor has given confidential data to trusted third parties (agents) .if some data is leaked and found in an unauthorized place. The distributor must analyze that the filtered data came from (where) one or more agents. We propose data allocation strategies to improve the probability of identifying leaks. We can also add "false object" to further improve our chances of detecting leaks and identify the culprit.
Data leakage is the unauthorized transmission of data or information from an organization to an external destination or recipient. Data leakage is defined as the accidental or intentional distribution of confidential or confidential data to an unauthorized entity. Confidential business and organizational data includes intellectual property, financial information, patient information, personal credit card data, and other information that is business and industry dependent. A data distributor has given this confidential data to a set of allegedly trusted agents (third parties). Some of the data is filtered and found in an unauthorized place. The distributor must evaluate the probability that the filtered data comes from one or more agents, rather than having been obtained independently by other means. We call the data owner to the distributor and the allegedly trusted third parties. Our goal is to detect when sensitive data from the distributor has been filtered by agents and, if possible, identify the agent that filtered the data.
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