Privacy-Preserving Detection of Sensitive Data Exposure
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Privacy-Preserving Detection of Sensitive Data Exposure

Project period

09/12/2019 - 10/29/2019

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31

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Privacy-Preserving Detection of Sensitive Data Exposure
Privacy-Preserving Detection of Sensitive Data Exposure
  •  In recent years, Data leakage is a big challenge in front of the research institutions and government organizations.
  •  Though there is a number of systems designed for data security by using different encryption algorithms, there is a big issue of the integrity of the users of those systems.
  •  It is very hard for any system administrator to trace out the data leaker and data leakage among the system users and data.
  • We propose a privacy-preserving data-leak detection solution and watermarking techniques that can be outsourced and be deployed in a semi-honest detection environment. 

Statistics from security firms, research institutions and government organizations show that the number of data-leak instances have grown rapidly in recent years. Among various data-leak cases, human mistakes are one of the main causes of data loss. There exist solutions detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. A common approach is to screen content in storage and transmission for exposed sensitive information. Such an approach usually requires the detection operation to be conducted in secrecy. However, this secrecy requirement is challenging to satisfy in practice, as detection servers may be compromised or outsourced. In this paper, we present a privacy-preserving data-leak detection (DLD) solution to solve the issue where a special set of sensitive data digests is used in detection. The advantage of our method is that it enables the data owner to safely delegate the detection operation to a semihonest provider without revealing the sensitive data to the provider. We describe how Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. The evaluation results show that our method can support accurate detection with very small number of false alarms under various data-leak scenarios.

Why: Problem statement

In recent years, Data leakage is a big challenge in front of research institutions and government organizations. Though there is the number of systems designed for data security by using different encryption algorithms, there is a big issue of the integrity of the users of those systems.

How: Solution description

We propose a privacy-preserving data-leak detection solution and watermarking techniques that can be outsourced and be deployed in a semi-honest detection environment. 

We abstract the privacy-preserving data-leak detection problem with a threat model, a security goal and a privacy goal. First we describe the two most important players in our abstract model: the organization (i.e., data owner) and the data-leak detection (DLD) provider. • Organization owns the sensitive data and authorizes the DLD provider to inspect the network traffic from the organizational networks for anomalies, namely inadvertent data leak. However, the organization does not want to directly reveal the sensitive data to the provider. • DLD provider inspects the network traffic for potential data leaks. The inspection can be performed offline without causing any real-time delay in routing the packets. However, the DLD provider may attempt to gain knowledge about the sensitive data

How is it different from competition

  • Excellent written and oral communication skills, Committed with the ability to work well in groups and under pressure.
  • Accepting responsibility, Quick learner.
  •  Hardworking and punctual.

Who are your customers

  • Computer technology system
  • Hacking systems

Project Phases and Schedule

Network security, data leak, privacy, collection intersection, fake records, leakage model. The data leakage detectors compute fingerprints from network traffic and identify the leak in them. The purpose of this system is to identify the data leakage of sensitive data of the files or any documents. In order to avoid the leakage of sensitive data, one can add random noise or can replace the values with some ranges.

A privacy-preserving data-leak detection model and present its realization. Using special digests, the exposure of the sensitive data is kept to a minimum during the detection. We have conducted extensive experiments to validate the accuracy, privacy, and efficiency of our solutions. For future work, we plan to focus on designing a host-assisted mechanism for the complete data-leak detection for large-scale organizations.

Resources Required

Software: Asp.net

Hardware: Ram 36

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