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Data leakage is defined as the accidental or intentional distribution of private or sensitive data to an unauthorized entity. Sensitive data of companies and organization includes intellectual property, financial information, patient information, personal credit card data and other information depending upon the business and the industry.
Furthermore, in many cases, sensitive data shred among various stakeholders such as employees working from outside the organizational premises, business partners and customers. This increases the risk of confidential information falling into unauthorized hands. Furthermore, in many cases, sensitive data is shared among various stakeholders such as employees working from outside the organizational premises e.
In the course of doing business, sometimes data must be handed over to supposedly trusted third parties for some enhancement or operations.
Similarly a company may have partnership with other companies that require sharing of customer data. Another enterprise may outsource its data processing, so data must be given to various other companies.
Perturbation and watermarking are techniques which can help in such situations. Perturbation is a very useful technique where the data is modified and made less sensitive before being handed to agents.
For example, one can add random noise to certain attributes or one can replace exact values by ranges on the original record. However in some cases, it is not important to alter the original record.
Suppose if an outsourcer is doing our payroll, he must have the exact salary and customer bank account numbers. If medical researchers treating the patients as opposed to simply computing statistic they may need accurate data for the patients.
Traditionally, leakage detection is handled by the watermarking. For example a unique code is embedded in each distributed copy. If that copy is later found in the hands of an unauthorized party, the leaker can be identified.
Watermarks can be very useful in some cases but again, involve some modification of the original data. Furthermore, watermarks can sometimes be destroyed if the data recipient is malicious [research paper].
There are some disadvantages of it. In this paper, we develop an algorithm of data allocation strategies for finding the guilty agents that improves the chances of identifying a leaker.
We also consider the option of adding fake objects to the distributed set. Such object do not corresponds to real entities but appear realistic to the agents. Means that fake objects act as a type of watermarks for the entire set, without modifying any original data. If it turns out that an agent was given one or more fake objects that were leaked, then the distributor can be more confident that agent was guilty.
Let the no of agents be A1, A2,Data Leakage Detection Panagiotis Papadimitriou, Student Member, IEEE, and Hector Garcia-Molina, Member, IEEE Abstract—We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). “realistic but fake” data record to further improve our changes of detecting leakage and identifying the guilty party.
The algorithms implemented using fake objects .
1.A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). regardbouddhiste.com of the data are leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop).
regardbouddhiste.com propose data allocation strategies (across the agents) that improve the probability of identifying leakages. regardbouddhiste.com methods do not 1/5(2). DATA LEAKAGE DETECTION ABSTRACT: A data distributor has given sensitive data to a set of supposedly trusted agents (third parties).
Some of the data is leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). The distributor must assess the likelihood that the leaked data came from one or more [ ]. Data leakage is a big problem in machine learning when developing predictive models.
Data leakage is when information from outside the training dataset is used to create the model. In this post you will discover the problem of data leakage in predictive modeling.
While data loss and data leakage can both result in a data breach, the detection and handling of data loss prevention and data leakage prevention must both be considered. Data loss prevention focuses on the detection and prevention of sensitive data exfiltration and/or lost data, and includes use cases from a lost or stolen thumb drive, to ransomware attacks.