PIMCity Demonstration


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Personal-Privacy Preserving Analytics


The Personal Privacy Preserving Analytics (P-PPA) module has the goal of allowing data analysts and stakeholders to extract useful information from the raw data while preserving the privacy of the users whose data is in the datasets. It leverages concepts like Differential Privacy and K-Anonymity so that data can be processed and shared while guaranteeing privacy for the users

P-PPA is capable to handle different sources of data inputs, that define which kind of privacy property is called into account: we have design solutions for tabular and batch stream, handled with PostgreSQL, MongoDB, and CSV modules, and live stream data.

Test the API
They can be tested through a test notebook.
Download a Test Notebook
Source Code
The project is open-source and its code is on the online repository:
The P-DS is distributed under AGPL-3.0-only, see the LICENSE file in the project repository.
Copyright (C) 2021 Politecnico di Torino - Martino Trevisan, Marco Mellia, Nikhil Jha, Giovanni Camarda, Luca Vassio

Demonstration video
In this video we illustrate the PPPA API and use a test notebook to:
  • Compute Privacy-Preserving statistical aggregates (such as the mean) using the Differential Privacy.
  • Anonymize a stream of data using the z-anonymity algorithm. It ensures that rare elements in the stream are anonymized to prevent user re-identification.