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  KEYWORD

DESIGN OF PRIVACY-PRESERVING ANALYTICS

keywords MACHINE LEARNING, PRIVACY

Reference persons MARCO MELLIA

External reference persons Martino Trevisan martino.trevisan@polito.it

Research Groups SmartData@PoliTO, Telecommunication Networks Group

Thesis type ANALYTICS DESIGN, DESIGN AND ENGINEERING

Description Web economy has been revolutionized by unprecedented possibility of collecting massive amounts of user personal data, which lead the web to become the largest data market and created the biggest companies in our history. Unfortunately, this change has deep consequences for users, who, deprived of any negotiation power, are compelled to blindly provide their data for free access to services. Data collection is opaque, fragmented and disharmonic,

The goal of this thesis is to design and implement Privacy-Preserving Analytics. They are means to impose control on personal data. The student will use concepts like Zero Knowledge, Differential Privacy, K-Anonymity to achieve the goal if designing and engineering solutions that allow one to share their data, while keeping control on it.

The student will work with the SmartData@Polito center, in the context of the European Project PIMCity (https://www.pimcity-h2020.eu/). The student will be involved in an dynamic context with 12 international partners, and may have the oppurtunity to participate to international meetings. Succesful students will have the possibility to continue their work as PhD students or research assistants.

Required skills Programming tools for Data Science (Python, Pandas)
Web programming (REST, PHP)
Good level of English
Priority will be given to Computer Engineering students with high marks (>27/30)


Deadline 10/12/2020      PROPONI LA TUA CANDIDATURA




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