Privacy Award Personal Data Storage (PDS)


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The PDS study has focused on enforcing data security preferences as well as how to securely encrypt when encrypted in PDS, which is privacy-aware on third-party demands to see consumer preferences. Will decide PDS introduced a major shift in users. Manage and monitor their private information by switching from service-central models to user-centric models.


Currently, our digital personal data is distributed through multiple Internet networks operated by various companies. (Eg, electronic social networking, clinics, banks, etc.) Through this case consumers on one hand lack the power of any data and, on the other, cannot fully manipulate their data because each vendor is a Takes a different view. Designing a privacy-conscious personal database management system requires further investigation even when identifying active learning is also an important step. This means that PDS capable of automatically making privacy decisions should be investigated on requests for third-party use.

Current System:

In current work, the system does not have robust techniques for implementing privacy-aware personal data storage. The system does not have active learning, which are the most representative examples to be selected from training datasets that are labeled by users.

proposed system:

More research is needed for PDS to be able to make individual privacy-conscious decisions on access requests by third parties. The system provides a new version to increase the confidentiality of the suggested string section learning algorithms in the system.

System Requirements:

S / W Requirements
  • Operating System: Windows XP
  • Coding Language: Java / J2EE
  • Front End: J2EE
  • Back End: MySQL
H / W Requirements
  • Processor: P-1V
  • RAM: 4 GB
  • Hard disk: 20GB
  • Monitor: SVGA15


  • Data over
  • EHealthcare CLOUDSERVER
  • Rights
  • end user


  • unit testing: During this test, each module is tested individually.
  • integration testing: Integration testing addresses issues associated with the dual problems of verification and program creation.
  • user acceptance test: User acceptance of a system is the key factor for the success of any system.
  • Output test: The output test is nothing but whether or not we have obtained the correct output for the given project.

conclusion and Future Work:

The article shows a privacy-aware personal storage space capable of dynamically making privacy-conscious decisions on third-party access requests to comply with consumer desires.

The program relies on active learning supplemented by technology to improve user privacy. As described in the article, we perform several tests on a practical dataset.

Example of privacy- aware personal data collection: prescription to the patient. Patient details are kept confidential and the patient allows access to the specific physician for treatment.

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