How can regulations be strengthened to prevent data misuse and ensure privacy?
Federated Learning (FL) and Traditional Machine Learning (TML) differ significantly in data handling and privacy: Traditional Machine Learning: Centralized Data: TML requires collecting and storing all data in a central server. Privacy Concerns: Centralizing data can expose it to security risks andRead more
Federated Learning (FL) and Traditional Machine Learning (TML) differ significantly in data handling and privacy:
Traditional Machine Learning:
Centralized Data: TML requires collecting and storing all data in a central server.
Privacy Concerns: Centralizing data can expose it to security risks and privacy breaches.
Federated Learning:
Decentralized Data: FL allows models to be trained across multiple devices without transferring raw data to a central server.
Enhanced Privacy: By keeping data on local devices and only sharing model updates, FL reduces the risk of data breaches and enhances user privacy.
Privacy Enhancement:
Data Minimization: FL minimizes the amount of data shared, limiting exposure.
Local Processing: Sensitive data stays on user devices, reducing the chance of unauthorized access.
As a Advisor, it’s important to recognize that FL offers a more privacy-conscious approach to machine learning by maintaining data on local devices and avoiding centralized data collection.
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The government should create a robust and independent Data Protection Authority (DPA) which can ensure effective enforcement of the Digital Personal Data Protection Act, 2023. The DPA should have the authority to investigate, audit, and impose penalties. The government should collaborate with globRead more
The government should create a robust and independent Data Protection Authority (DPA) which can ensure effective enforcement of the Digital Personal Data Protection Act, 2023. The DPA should have the authority to investigate, audit, and impose penalties.
The government should collaborate with global data protection authorities to standardize practices and address cross-border data challenges. This includes harmonizing laws with GDPR (European Union) and CCPA (California).
The government should educate citizens about their data rights and the importance of data privacy, empowering individuals to handle their data vigilantly. It should mandate stringent cybersecurity protocols, encouraging advanced encryption and regular security audits. Data localization can enhance security but should be balanced with the need for global data flow, using hybrid models to keep critical data within the country while allowing non-sensitive data to be transferred internationally.
The legislature should create specific regulations for sensitive sectors like healthcare, finance, and telecom, and regularly update data protection laws to keep pace with technological advancements and emerging threats. This approach prevents loopholes and ensures relevance. Additionally, promoting research in privacy-enhancing technologies and encouraging startups and tech companies to innovate solutions that prioritize data protection is essential.
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