PIMCity Demonstration


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Personal-Privacy Metrics

Personal Privacy Metrics (P-PM) represent the means to increase the user’s awareness. This component collects, computes and shares easy-to-understand data to allow users know how a service (e.g., a data buyer) stores and manages the data, if it shares it with third parties, how secure and transparent it looks, etc. These are all fundamental pieces of information for a user to know to take informed decisions. The PM computes this information via a standard REST interface, offering an open knowledge information system which can be queried using an open and standard platform. PMs combine information from supervised machine learning analytics, services themselves and domain experts, volunteers, and contributors. Its implementation builds on MongoDB (for the database), Python/Flask and Swagger (for the server).
Access the Web UI
It is possible to access the Web Interface of the Personal-Privacy Metrics by clicking the button below.
Go to the online Demonstrator
Source Code
The project is open-source and its code is on the online repository:
The P-PM is distributed under AGPL-3.0-only, see the LICENSE file in the project repository.
Copyright (C) 2021 Ermes Cyber Security S.R.L.

Demonstration video
In this video we show how to:
  • Check the status of the system with GET /health API.
  • Insert a new Privacy Metric with POST /privacy-metrics API.
  • Download an existing Privacy Metric with GET /privacy-metrics/{name}/ API.
  • Modify a section of an existing Privacy Metric with PUT /privacy-metrics/{name}/provided_information API.
The video also shows how Scores are updated as soon as the Privacy Metric is created of modified.