About Anne Josiane Kouam: 
Her research delves into the crucial areas of mobile security and privacy, which have become increasingly significant due to the pervasive access provided by mobile networks. As these networks connect a growing number of users and devices, the potential for attacks escalates. Her work addresses several key issues:

• Bypass fraud: This involves fraudsters redirecting international calls to appear as local calls within the destination country. Apart from causing substantial financial losses, this attack compromises users' privacy by allowing fraudsters to eavesdrop on international phone conversations.
• Phishing attacks: Traditionally associated with email communication, phishing attacks have expanded to include text messages on mobile networks, known as "smishing" attacks. This broadens their reach and makes it easier to deceive unsuspecting individuals.
• User tracking attacks: Leveraging mobile devices' pervasiveness, these attacks infer users' locations through accessible information without explicit permission.

Furthermore, the widespread reach of mobile networks ensures the comprehensiveness of mobile network datasets, such as Charging Data Records, which include users' mobile service usage and location information. While valuable for research across various domains, these datasets also pose challenges due to the inclusion of sensitive user information. Research explores the use of deep generative models to create privacy-preserving realistic mobile datasets. This includes:

• Investigating the vulnerability of released mobile datasets to de-anonymization attacks.
• Investigating privacy leaks of generative models.
• Evaluating the trustworthiness of crowd-sourced mobile network datasets.

Anne Josiane’s investigations span both offensive and defensive strategies, exploring the effectiveness of attacks, particularly when strengthened by recent advancements in machine learning. This comprehensive approach not only enhances the understanding of these threats but also paves the way for innovative, widely applicable machine learning-driven defenses.