Visible to the public Privacy and Security of Big Data in AI Systems: A Research and Standards Perspective

TitlePrivacy and Security of Big Data in AI Systems: A Research and Standards Perspective
Publication TypeConference Paper
Year of Publication2019
AuthorsDilmaghani, Saharnaz, Brust, Matthias R., Danoy, Grégoire, Cassagnes, Natalia, Pecero, Johnatan, Bouvry, Pascal
Conference Name2019 IEEE International Conference on Big Data (Big Data)
Date Publisheddec
KeywordsAI, AI systems developments, artificial intelligence, artificial intelligence systems, Big Data, big data privacy, big data security, countermeasures, Data models, data privacy, defense strategies, Human Behavior, human factors, IEC standards, Metrics, privacy, pubcrawl, resilience, Resiliency, Scalability, SDOs, security, security of data, Standards Developing Organizations

The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.

Citation Keydilmaghani_privacy_2019