The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

2022 ◽  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Security And Privacy ◽  
Iot Security ◽  
International Conference

Edge Learning for Distributed Big Data Analytics

2022 ◽  
Author(s):  
Song Guo ◽  
Zhihao Qu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
System Design ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Security And Privacy ◽  
Learning Performance ◽  
Learning Models ◽  
Key Technologies ◽  
Machine Learning Models

Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.

Biases in machine learning models and big data analytics: The international criminal and humanitarian law implications

2020 ◽  
Vol 102 (913) ◽  
pp. 199-234
Author(s):  
Nema Milaninia
Keyword(s):  
Machine Learning ◽  
Human Rights ◽  
Big Data ◽  
Data Analytics ◽  
International Criminal ◽  
Big Data Analytics ◽  
International Criminal Law ◽  
Gender Inequalities ◽  
Humanitarian Law ◽  
Mass Graves

AbstractAdvances in mobile phone technology and social media have created a world where the volume of information generated and shared is outpacing the ability of humans to review and use that data. Machine learning (ML) models and “big data” analytical tools have the power to ease that burden by making sense of this information and providing insights that might not otherwise exist. In the context of international criminal and human rights law, ML is being used for a variety of purposes, including to uncover mass graves in Mexico, find evidence of homes and schools destroyed in Darfur, detect fake videos and doctored evidence, predict the outcomes of judicial hearings at the European Court of Human Rights, and gather evidence of war crimes in Syria. ML models are also increasingly being incorporated by States into weapon systems in order to better enable targeting systems to distinguish between civilians, allied soldiers and enemy combatants or even inform decision-making for military attacks.The same technology, however, also comes with significant risks. ML models and big data analytics are highly susceptible to common human biases. As a result of these biases, ML models have the potential to reinforce and even accelerate existing racial, political or gender inequalities, and can also paint a misleading and distorted picture of the facts on the ground. This article discusses how common human biases can impact ML models and big data analytics, and examines what legal implications these biases can have under international criminal law and international humanitarian law.

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