New Details

The concept of privacy in the digital age is subjective and multifaceted, presenting significant challenges due to diverse interpretations and inadequate interfaces for comprehending risks. This thesis introduces the DevPrivOps methodology, based on privacy quantification frameworks to ensure robust compliance throughout the SDLC. As part of this effort, we evaluated the effectiveness of Differential Privacy (DP) for quantifying privacy and developed a privacy quantification model closely aligned with the stages of the DevPrivOps methodology. During the development analysis phase, we introduced PsDC-a hierarchical, multidimensional framework centered on user privacy preferences. We further proposed automated data classification techniques based on PsDC; developed PrivGuide, a tool designed to assist developers during privacy assessments; ASAP, a tool for detecting malicious application permissions; conducted privacy threat analysis through the semantic interpretation of privacy policies; and proposed a privacy preferences manager. To enhance usability, we addressed the need for accessible privacy visualizations by proposing SCALE, an application prototype to visualize the privacy level. This thesis makes a significant contribution to advancing the state of digital privacy by bridging engineering practices, user empowerment, and regulatory compliance.

Speaker: Catarina Silva (IEETA, PhD candidate in Computer Science)
This seminar will take place on 4 December 14:00 at the IEETA building.
The public defense of the PhD work is scheduled for 17 December at 10:00 in the ISCA-UA auditorium.