Defending Emotional Privacy with Adversarial Machine Learning for Social Good

Shawqi Al-Maliki*, Mohamed Abdallah*, Junaid Qadir, Ala Al-Fuqaha*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Protecting the privacy of personal information, including emotions, is essential, and organizations must comply with relevant regulations to ensure privacy. Unfortunately, some organizations do not respect these regulations, or they lack transparency, leaving human privacy at risk. These privacy violations often occur when unauthorized organizations misuse machine learning (ML) technology, such as facial expression recognition (FER) systems. Therefore, researchers and practitioners must take action and use ML technology for social good to protect human privacy. One emerging research area that can help address privacy violations is the use of adversarial ML for social good. Evasion attacks, which are used to fool ML systems, can be repurposed to prevent misused ML technology, such as ML-based FER, from recognizing true emotions. By leveraging adversarial ML for social good, we can prevent organizations from violating human privacy by misusing ML technology, particularly FER systems, and protect individuals' personal and emotional privacy. In this work, we propose an approach called Chaining of Adversarial ML Attacks (CAA) to create a robust attack that fools misused technology and prevents it from detecting true emotions. To validate our proposed approach, we conduct extensive experiments using various evaluation metrics and baselines. Our results show that CAA significantly contributes to emotional privacy preservation, with the fool rate of emotions increasing proportionally to the chaining length. In our experiments, the fool rate increases by 48% in each subsequent chaining stage of the chaining targeted attacks (CTA) while keeping the perturbations imperceptible (ϵ = 0.0001).

Original languageEnglish
Title of host publication2023 International Wireless Communications and Mobile Computing, IWCMC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages345-350
Number of pages6
ISBN (Electronic)9798350333398
DOIs
Publication statusPublished - 2023
Event19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 - Hybrid, Marrakesh, Morocco
Duration: 19 Jun 202323 Jun 2023

Publication series

Name2023 International Wireless Communications and Mobile Computing, IWCMC 2023

Conference

Conference19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023
Country/TerritoryMorocco
CityHybrid, Marrakesh
Period19/06/2323/06/23

Keywords

  • Emotional-Privacy Preservation
  • Evasion Attacks for Good
  • Robust Adversarial ML attacks

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