PAL Research Group

Privacy AnaLytics (PAL) Research Group

Current Projects

In line with my research interests, I study various problems that fall under the banner of privacy analytics. My main goal is to design algorithms and models to analyse data to determine whether or not privacy violating information exists therein. This is especally important in enabling privacy preserving data analytics and/or machine learning. I also study issues related to usability, education, and energy efficiency in relation to designing computational and human-centered privacy solutions.

Auto-PII: Automated PII Discovery in Mesh Data

We (Rakibul Islam, Hasan Shahriar, Abrar Kamal, Igor Fialko, Imtiaz Ahmed, and Prof A. Kayem) are working with Cimpress GmbH to develop efficient and accurate machine learning models to enable discovering personal identifying information in data meshes. Publications available on DBLP.

Algorithms, Code, and Data:

Outlier Detection in Textual Data

Sec-Edu: Security Education and Usability

Working with Igor Fialko, Tim Depping, and Johannes Klemm, my goal in this project is to design a suite of security/privacy education tools to assess effectiveness in raising user awareness on security/privacy pitfalls in the digital environment. This project is funded by the HPI foundation.

Algotihms, Code, and Data:

Ethical Hacking (Contact us for login instructions)

Recent Past Project - Highlights

Quasi-Identifier Discovery in High Dimensional Data

Here, our (Dr. N.J. Podlesny (PhD Thesis) and Prof. A. Kayem) focus was on designing efficient algorithms to enable quasi-identifier discovery in large high dimensional data. Application areas for this work include genome data, health data, and online shopping data. Publications available on DBLP.

Algorithms, Code, and Data: