A New Approach for Dynamic and Risk-Based Data Anonymization

The way people lead their lives is considered an important factor in health. In this chapter, we describe a system to provide risk assessment based on behavior for the health insurance sector. The system processes real-world data (RWD) of individuals from their daily life that enumerate different aspects of behavior collection. The data have been captured using the Healthentia platform and a simulator that augments the actual dataset with synthetic data. Classifiers are built to predict variations of peoples’ well-being short-term outlook. Risk assessment services are provided to health insurance professionals by processing the classifier predictions in the long term while explaining the classifiers themselves provide insights on the coaching of the users of the service.

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Risk Assessment for Personalized Health Insurance Products

Pnevmatikakis¹,

Kanavos²,

Perikleous³

et al. 2022

Big Data and Artificial Intelligence in Digital Finance

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Large Scale Data Anonymisation for GDPR Compliance

Ortega-Fernandez

Martinez

Orellana

2021

Big Data and Artificial Intelligence in Digital Finance

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General Data Protection Regulation (GDPR) has been in place since May 2018 to give EU citizens more control over their personal data, applying principles like security and privacy by design. One of the most powerful tools to allow data processing while being compliant with data protection regulations is anonymisation, a procedure that consists of transforming data in such a way that makes no longer possible the re-identification of the data subjects. This chapter describes how anonymisation can be performed at a large scale, addressing common challenges to become GDPR compliant.

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Challenges of Developing AI Applications in the Evolving Digital World and Recommendations to Mitigate Such Challenges

Vaidyanathan

Sivakumar²,

Kaliamourthy³

2021

Advances in Digital Crime, Forensics, and Cyber Terrorism

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These intelligence in the systems are not organic but programmed. In spite of being extensively used, they suffer from setbacks that are to be addressed to expand their usage and a sense of trust in humans. This chapter focuses on the different hurdles faced during the course of adopting the technology namely data privacy, data scarcity, bias, unexplainable Blackbox nature of AI, etc. Techniques like adversarial forgetting, federated learning approach are providing promising results to address various issues like bias, data privacy are being researched widely to check their competency to mitigate these problems. Hardware advancements and the need for enhancing the skillset in the artificial intelligence domain are also elucidated. Recommendations to resolve each major challenge faced are also addressed in this chapter to give an idea about the areas that need improvement.

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A New Approach for Dynamic and Risk-Based Data Anonymization

Cited by 3 publications

References 13 publications

Risk Assessment for Personalized Health Insurance Products

Risk Assessment for Personalized Health Insurance Products

Large Scale Data Anonymisation for GDPR Compliance

Challenges of Developing AI Applications in the Evolving Digital World and Recommendations to Mitigate Such Challenges

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