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Data leakage detection

Unauthorized transmission of sensitive data within or external destinations

Data leakage detection

  1. 1. Submitted by: SUVEEKSHA JAIN Mtech I Sem SJEC
  2. 2.  Data leakage is the unauthorized transmission of sensitive data or information from within an organization to an external destination or recipient.  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.
  3. 3.  In the course of doing business, sometimes data must be handed over to trusted third parties for some enhancement or operations.  Sometimes these trusted third parties may act as points of data leakage.  Example: a) A hospital may give patient records to researcher who will devise new treatments. b) A company may have partnership with other companies that require sharing of customer data. c) An enterprise may outsource its data processing , so data must be given to various other companies.
  4. 4. Development chains Supply chains Outsourcing Business hubs Demand chains
  5. 5. Sept. 2011 Science Applications International Corp Backup tapes stolen from a car containing 5,117,799 patients’ names, phone numbers ,Social Security numbers, and medical information. July 2008 Google Data were stolen, not from Google offices, but from the headquarters of an HR outsourcing company ,Colt Express. July 2009 American Express DBA stole a laptop containing thousands of American Express card numbers. The DBA reported it stolen Aug. 2007 Nuclear Laboratory in Los Alamos An employee of the U.S. nuclear laboratory in Los Alamos transmitted confidential information by email. Data leakage incidents
  6. 6.  Owner of data is termed as the distributor and the third parties are called as the agents .  In case of data leakage, the distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means.
  7. 7. SYSTEM DIAGRAM
  8. 8. Watermarking Overview: A unique code is embedded in each distributed copy. If that copy is later discovered in the hands of an unauthorized party, the leaker can be identified. Mechanism: The main idea is to generate a watermark [W(x; y)] using a secret key chosen by the sender such that W(x; y) is indistinguishable from random noise for any entity that does not know the key (i.e., the recipients).
  9. 9.  The sender adds the watermark W(x; y) to the information object I(x; y) and thus forms a transformed object TI(x; y) before sharing it with the recipient(s).  It is then hard for any recipient to guess the watermark W(x; y) (and subtract it from the transformed object TI(x; y));  The sender on the other hand can easily extract and verify a watermark (because it knows the key).
  10. 10.  It involves some modification of data that is making the data less sensitive by altering attributes of the data.  The second problem is that these watermarks can be sometimes destroyed if the recipient is malicious.
  11. 11.  Using data allocation strategies, the distributer give data to agents in order to improve the chances of detecting guilty agents.  Fake object is added to identify the guilty party.  Distributer will be more confident when data leaked by agents and they may stop doing business with him.
  12. 12. ARCHITECTURAL VIEW OF THE SYSTEM
  13. 13. Thus we need a data leakage detection technique which fulfils the following objective and abides by the given constraint. CONSTRAINT To satisfy agent requests by providing them with the number of objects they request or with all available objects that satisfy their conditions. Avoid perturbation of original data before handing it to agents OBJECTIVE To be able to detect an agent who leaks any portion of his data.
  14. 14. Different WaterMarking system:  Embedding and extraction  Secure speed spectrum Watermarking  DCT-Based Watermarking  Speed spectrum  Wavelet based Watermarking  Robust watermarking technique  Invisible watermarking  Watermarking of digital audio and image using Matlab  Watermarking while preserving the critical path  Buyer seller watermarking protocols  Watermarking using cellular automata transform
  15. 15.  Fragile watermarking
  16. 16.  Data Allocation Module  Fake Object Module  Data Distributor Module  Agent guilt Module
  17. 17.  Data Allocation: The main focus of our project is the data allocation problem as how can the distributor “intelligently” give data to agents in order to improve the chances of detecting a guilty agent.  Fake Object: Fake objects are objects generated by the distributor in order to increase the chances of detecting agents that leak data. The distributor may be able to add fake objects to the distributed data in order to improve his effectiveness in Detecting guilty agents. Our use of fake objects is inspired by the use of “trace” records in mailing lists.
  18. 18.  Data Distributor: 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 agents, as opposed to having been independently gathered by other means.  Agent guilt: To compute prfgiijsg,we need an estimate for the probability that values in S can be “guessed”by target.
  19. 19.  Cloud is large group of interconnected computers. Any authorized user can access these apps from any computer over internet. Key properties of cloud computing:  User centric  Task centric  Powerful  Accessible  Intelligent  programmable
  20. 20.  Right protection is provided for relational data  Watermarking technique for multimedia data  Achieving K-Anonymity Privacy Protection  Watermarking the relational databses  Lineage tracing general data warehouse transformations
  21. 21.  The presented strategies assume that there is a fixed set of agents with requests known in advance.  The distributor may have a limit on the number of fake objects.
  22. 22.  Cloud computing technology enables data to be stored in the cloud and enables users both inside and outside the company to access the same data which increases the usefulness of data
  23. 23.  It helps in detecting whether the distributor’s sensitive data has been leaked by the trustworthy or authorized agents.  It helps to identify the agents who leaked the data.  Reduces cybercrime.
  24. 24.  Though the leakers are identified using the traditional technique of watermarking, certain data cannot admit watermarks.  In spite of these difficulties, it is possible to assess the likelihood that an agent is responsible for a leak.  We observed that distributing data judiciously can make a significant difference in identifying guilty agents using the different data allocation strategies.
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Unauthorized transmission of sensitive data within or external destinations

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