Date of Award

10-30-2020

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Computer Science

First Advisor

Saeed Samet

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

With the recent advances in data analytics and machine learning, organizations are becoming more and more interested in utilizing these techniques to generate insights from the data they have. But the biggest hurdle, especially for those organizations that collect private information, is that it becomes challenging to share their data with data analysts without compromising the privacy of the data. Differential privacy helps to share private data with provable guarantees of privacy for individuals. Even though differential privacy is very good at preserving privacy, it still poses a lot of burden on data analysts to understand differential privacy and its intricate algorithms. Moreover, this also doesn't give any accuracy guarantees to the data analyst. Keeping this in mind, APEx (Accuracy-Aware Differentially Private Data Exploration) was introduced in May 2019, which allows data analysts to run a sequence of queries keeping privacy and accuracy in place. APEx was implemented for only one table in the database. In this research, it is extended and evaluated on multiple table queries.

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