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KDk: A Novel Machine Learning Framework that Protects Vertical Federated Learning from All the Known Types of Label Inference Attacks with Very High Performance

 KDk: A Novel Machine Learning Framework that Protects Vertical Federated Learning from All the Known Types of Label Inference Attacks with Very High Performance

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Federated Learning: Practical Solutions and Value

Introduction to Federated Learning

Federated Learning (FL) is a cutting-edge technology that allows collaborative model training without sharing raw data. It enables organizations and individuals to work together on model development while protecting sensitive data.

Practical Solutions and Value

FL reduces communication costs and integrates diverse datasets while maintaining the unique characteristics of each participant’s data. However, it poses risks of indirect information leakage, especially during model aggregation.

Data Partition Strategies

FL employs various data partition strategies, including Horizontal FL (HFL), Vertical FL (VFL), and Transfer Learning, each with specific advantages:

  • Horizontal FL: Suitable for regional branches of the same business aiming to build a richer dataset.
  • Vertical FL: Involves non-competing entities with vertically partitioned data sharing overlapping data samples.
  • Transfer Learning: Applicable when there is little overlap in data samples and features among multiple subjects with heterogeneous distributions.

Defending Against Label Inference Attacks

To address privacy concerns in FL, researchers at the University of Pavia developed a defense mechanism called KD𝑘 (Knowledge Discovery and 𝑘-anonymity).

KD𝑘 Framework

KD𝑘 relies on Knowledge Distillation (KD) and an obfuscation algorithm to enhance privacy protection. It uses a teacher network to generate soft labels and adds uncertainty through 𝑘-anonymity, making it challenging for attackers to infer the most probable label accurately.

Value and Efficacy

The experimental findings demonstrate a notable reduction in the accuracy of label inference attacks, validating the efficacy of the proposed defense mechanism. The research offers a robust countermeasure tailored to combat label inference attacks and outperforms existing defense strategies.

AI Solutions for Business

For companies looking to evolve with AI, practical steps include identifying automation opportunities, defining KPIs, selecting AI solutions, and implementing them gradually. For AI KPI management advice and insights into leveraging AI, organizations can connect with the experts.

Spotlight on a Practical AI Solution

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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