Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Ranbaduge, Thilina"'
Autor:
Yang, Mengmeng, Qu, Youyang, Ranbaduge, Thilina, Thapa, Chandra, Sultan, Nazatul, Ding, Ming, Suzuki, Hajime, Ni, Wei, Abuadbba, Sharif, Smith, David, Tyler, Paul, Pieprzyk, Josef, Rakotoarivelo, Thierry, Guan, Xinlong, M'rabet, Sirine
The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences within an intelligent digital ecosystem where artificial intelligence (A
Externí odkaz:
http://arxiv.org/abs/2410.21986
Publikováno v:
Forty-Fifth International Conference on Information Systems, Bangkok, Thailand 2024
The absence of data protection measures in software applications leads to data breaches, threatening end-user privacy and causing instabilities in organisations that developed those software. Privacy Enhancing Technologies (PETs) emerge as promising
Externí odkaz:
http://arxiv.org/abs/2410.00661
Financial crimes like terrorism financing and money laundering can have real impacts on society, including the abuse and mismanagement of public funds, increase in societal problems such as drug trafficking and illicit gambling with attendant economi
Externí odkaz:
http://arxiv.org/abs/2408.09935
Autor:
Supeksala, Yasas, Nguyen, Dinh C., Ding, Ming, Ranbaduge, Thilina, Chua, Calson, Zhang, Jun, Li, Jun, Poor, H. Vincent
The rise of Artificial Intelligence (AI) has revolutionized numerous industries and transformed the way society operates. Its widespread use has led to the distribution of AI and its underlying data across many intelligent systems. In this light, it
Externí odkaz:
http://arxiv.org/abs/2402.06682
With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. The widespread exposure of such location data poses significant privacy risks to users, as it
Externí odkaz:
http://arxiv.org/abs/2402.03612
In the absence of data protection measures, software applications lead to privacy breaches, posing threats to end-users and software organisations. Privacy Enhancing Technologies (PETs) are technical measures that protect personal data, thus minimisi
Externí odkaz:
http://arxiv.org/abs/2401.00879
Autor:
Ranbaduge, Thilina, Ding, Ming
A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models. Supervised learning approaches, such as deep learning techniques, generate high-quality ML functions for real-life applic
Externí odkaz:
http://arxiv.org/abs/2211.06782
Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privacy and confidentiality concerns
Externí odkaz:
http://arxiv.org/abs/2211.02161
Autor:
Wei, Kang, Li, Jun, Ma, Chuan, Ding, Ming, Wei, Sha, Wu, Fan, Chen, Guihai, Ranbaduge, Thilina
Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user devices, however with the increasing concerns on users' privacy. As a s
Externí odkaz:
http://arxiv.org/abs/2202.04309
Autor:
Li, Yang, Purcell, Michael, Rakotoarivelo, Thierry, Smith, David, Ranbaduge, Thilina, Ng, Kee Siong
The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect
Externí odkaz:
http://arxiv.org/abs/2107.04245