Zobrazeno 1 - 10
of 15 468
pro vyhledávání: '"Data provenance"'
The Internet of Things (IoT) relies on resource-constrained devices deployed in unprotected environments. Given their constrained nature, IoT systems are vulnerable to security attacks. Data provenance, which tracks the origin and flow of data, provi
Externí odkaz:
http://arxiv.org/abs/2407.03466
Autor:
BOFENG PAN1 panbofeng@hotmail.com, STAKHANOVA, NATALIA1 natalia@cs.usask.ca, RAY, SUPRIO2 sray@unb.ca
Publikováno v:
ACM Computing Surveys. 2023 Suppl14s, Vol. 55, p1-35. 35p.
Identifying the origin of data is crucial for data provenance, with applications including data ownership protection, media forensics, and detecting AI-generated content. A standard approach involves embedding-based retrieval techniques that match qu
Externí odkaz:
http://arxiv.org/abs/2406.02836
Akademický článek
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Autor:
Kumar, Vijay, Paul, Kolin
Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of f
Externí odkaz:
http://arxiv.org/abs/2403.15522
Federated Learning (FL) presents a promising paradigm for training machine learning models across decentralized edge devices while preserving data privacy. Ensuring the integrity and traceability of data across these distributed environments, however
Externí odkaz:
http://arxiv.org/abs/2403.01451
Autor:
Ahmed, Mansoor1,2 (AUTHOR) amil.rohani@uokajk.edu.pk, Dar, Amil Rohani2,3 (AUTHOR), Helfert, Markus1 (AUTHOR), Khan, Abid4 (AUTHOR) a.khan3@derby.ac.uk, Kim, Jungsuk5,6 (AUTHOR) mansoor.ahmed@mu.ie
Publikováno v:
Sensors (14248220). Jul2023, Vol. 23 Issue 14, p6495. 26p.
Autor:
Longpre, Shayne, Mahari, Robert, Chen, Anthony, Obeng-Marnu, Naana, Sileo, Damien, Brannon, William, Muennighoff, Niklas, Khazam, Nathan, Kabbara, Jad, Perisetla, Kartik, Wu, Xinyi, Shippole, Enrico, Bollacker, Kurt, Wu, Tongshuang, Villa, Luis, Pentland, Sandy, Hooker, Sara
The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we
Externí odkaz:
http://arxiv.org/abs/2310.16787