Published Paper Details:

The advent of artificial intelligence (AI) and data-driven technologies brought new depths to cybersecurity where interpolated, or dumped data can equally serve as a valuable asset. The idea is named Trash Intelligence based on the view that AI tools utilize trash data to reveal sensitive information left behind by digital users; e.g., deleted files, caches, and logs. Although organizations believe that the data deleted is no longer readable, research indicates that digital waste can easily be retrieved, and therefore it exposes them to great threats of hacking and exploitation [4], [13]. Emerging digital and circular economy technologies, including blockchain and Internet of Things have been deployed successfully to physical and e-waste management [1], [8], yet there is a dearth of studies on the cybersecurity impact of digital dustbins. Machine learning methods have already been shown to be useful in waste forecasting, optimization and resource recovery [10], [5] implying that malicious actors may similarly use machine learning to extract sensitive information. This paper will discuss the possibility of AI tapping on digital residues and how it can be possibly attacked and the countermeasures. This gap is connected to the linkage of concepts in the three fields (waste management, circular economy, and cybersecurity). Therefore, the study identifies an actual research gap to secure the data disposal activities in the AI-driven setting.