At the same time, it increases the possibility of personal information misuse, reaching unprecedented levels of power and speed in analyzing and spreading individuals' data.
Machine learning employs algorithms to analyze data, improve performance, and enable AI to make decisions without human intervention.
These AI types often collaborate, posing challenges to data privacy.
AI collects data intentionally, where users provide information, or unintentionally through facial recognition.
The problem arises when unintentional data collection leads to unexpected uses, compromising privacy.
Discussing pet food or more intimate purchases around a phone can lead to targeted ads, revealing unintentional data gathering.
Traditional approaches to privacy and machine learning are centered mainly around two concepts: user control and data protection.
Data protection involves anonymized and encrypted data, but even here, the gaps are inevitable, especially in machine learning, where decryption is often necessary.
Trust is crucial when sharing digital assets, such as training data, inference data, and machine learning models across different entities.
Examples of Security Breaches As we rely more on communication technologies using machine learning, the chance of data breaches and unauthorized access goes up.
Hackers might try to take advantage of vulnerabilities in these systems to get hold of personal data, such as name, address, and financial information, which can result in fund losses and identity theft.
Federated learning allows separate entities to collectively train a model without sharing explicit data.
In turn, homomorphic encryption enables machine learning on encrypted data throughout the process and differential privacy ensures that calculation outputs cannot be tied to individual data presence.
In traditional methods, sensitive user data is sent to centralized servers for training, posing numerous privacy concerns, and federated learning addresses this by allowing models to be trained locally on devices, ensuring user data security.
Enhanced Data Privacy and Security Federated learning, with its collaborative nature, treats each IoT device on the edge as a unique client, training models without transmitting raw data.
Improved Data Accuracy and Diversity Another important issue is that centralized data used to train a model may not accurately represent the full spectrum of data that the model will encounter.
In contrast, training models on decentralized data from various sources and exposing them to a broader range of information enhances the model's ability to generalize new data, handle variations, and reduce bias.
These methods ensure that data stays encrypted and secure during communication and model aggregation.
The homomorphic encryption allows computations on encrypted data without decryption.
The server would then process that data without decrypting it, and then the user would get it back.
This Cyber News was published on feeds.dzone.com. Publication date: Fri, 09 Feb 2024 12:43:05 +0000