Machine Learning-Driven Convergence Analysis in Multijurisdictional Compliance Using BERT and K-Means Clustering
Digital data continues to grow, there has been a shift towards using effective regulatory mechanisms to safeguard personal information. The CCPA of California and the General Data Protection Regulation (GDPR) of the European Union are two of the most important privacy laws. The regulation is intended to safeguard consumer privacy, but it varies greatly in scope, definitions, and methods of enforcement. This paper presents a fresh approach to adaptive compliance, using machine learning and emphasizing natural language processing (NLP) as the primary focus of comparison between the GDPR and CCPA. Using NLP, this study compares various regulations to identify areas where they overlap or diverge. This includes the "right to be forgotten" provision in the GDPR and the "opt-out of sale" provision under CCPA. International companies can learn valuable lessons from this report, as it outlines strategies for better enforcement of laws across different nations. Additionally, the paper discusses the challenges of utilizing NLP in legal literature and proposes methods to enhance the model-ability of machine learning models for studying regulations. The study's objective is to "bridge the gap between legal knowledge and technical expertise" by developing regulatory compliance strategies that are more efficient in operation and more effective in data protection.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Machine Learning based Enterprise Financial Audit Framework and High Risk Identification
In the face of global economic uncertainty, financial auditing has become essential for regulatory compliance and risk mitigation. Traditional manual auditing methods are increasingly limited by large data volumes, compl…
Feature ImportanceFraud DetectionOFMU: Optimization-Driven Framework for Machine Unlearning
Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copyrighted materials, or outdated information, without retraining from scra…
Quantifying Security Vulnerabilities: A Metric-Driven Security Analysis of Gaps in Current AI Standards
As AI systems integrate into critical infrastructure, security gaps in AI compliance frameworks demand urgent attention. This paper audits and quantifies security risks in three major AI governance standards: NIST AI RMF…
Designing MacPherson Suspension Architectures using Bayesian Optimization
Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for complia…
Bayesian OptimizationEnhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection
Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, security, regulatory compliance, and cost-effi…
Federated Learning