AI Fort Knox Pro: Mastering Data Minimization And Security

About Course

Course Overview:

This course provides an in-depth understanding of data minimization and security in AI systems. It covers a range of topics from AI system security assessments and data integrity to advanced attack scenarios and privacy-preserving techniques. Designed for AI professionals, the course aims to enhance your ability to secure AI systems and ensure data privacy.

The course begins with foundational concepts of AI security, including key considerations when assessing AI systems and understanding data integrity. It explores common AI attack scenarios that compromise data integrity and offers practical steps to secure training data in machine learning (ML) systems.

Students will learn about model inversion and membership inference attacks, the dangers of AI model dependencies, and the impact of these attacks on privacy. The course also covers techniques to mitigate overfitting, manage external software use, and leverage open-source code libraries safely.

A significant portion of the course is dedicated to understanding and managing adversarial attacks, including white box and black box model attacks. It provides strategies for balancing AI functionality with data minimization and highlights the importance of a privacy-conscious approach to AI development.

Course Highlights:

Security Strategies: Learn seven key considerations when assessing AI systems’ security and steps to secure training data.

Advanced Attack Mitigation: Understand how to mitigate model inversion, membership inference attacks, and adversarial attacks.

Data Minimization Techniques: Explore five key techniques for achieving data minimization and balancing it with AI functionality.

Privacy-Preserving Methods: Discover six data-focused techniques for a privacy-conscious approach to AI development, and the importance of differential privacy.

Real-World Applications: Practical examples including healthcare data de-risking, federated learning in banking, and sales behavior prediction.

Key factors and considerations in AI security assessments.
Techniques to safeguard sensitive training data in AI systems.
How to handle vulnerabilities in open-source programming libraries.
Understanding and mitigating model inversion and membership inference attacks.
Differences and mitigation strategies for white box and black box model attacks.
Approaches to data minimization and integrating privacy into AI and ML design.
The significance of differential privacy and techniques like perturbation and controlled randomness.
Practical applications of federated learning and synthetic data generation.

Course Content

Section 1: AI Security Fundamentals

Seven Key Considerations When Assessing AI Systems’ Security

04:03
How to Inculcate an Understanding of Data Integrity Amongst Multidisciplinary AI Teams?

02:08
Four Key Factors That Impact AI Security

03:10
The Importance of Information Sharing Between ML Model Providers and Clients

02:00
The Hidden Dangers of ML Framework Dependencies

02:58
Recruitment Example of Vulnerability Exploit From Open-Source Programming Library

02:50
Three Key Measures to Safeguard Your ML Deployment Pipeline

02:28
LAWGAMES: AI FORT KNOX PRO

Section 2: Data Integrity and Attack Scenarios

Four AI Attack Scenarios That Compromise Data Integrity

03:08
Seven Steps to Secure Training Data in ML Systems

04:08
Three Techniques to Safeguard Sensitive Training Data in AI Systems

03:05
Six Key Considerations When Using External Software In Your AI

04:10
Benefits of Using Open Source Code Libraries In Your AI

02:58
Hidden Data Dangers of Support Vector Machines and K-Nearest Neighbors

02:26
De-Risking Healthcare Data In SVM or KNN Models

02:52
Personal Data Within SVM and KNN Models – is it an Attack Vector?

02:39
LAWGAMES: AI FORT KNOX PRO

Section 3: Model Inversion and Membership Inference Attacks

An Introduction to Model Inversion and Membership Inference Privacy Attacks

03:49
Understanding Model Inversion Attacks

02:00
Anatomy of an AI Model Inversion Attack in Healthcare

02:16
Anatomy of a Model Inversion Attack on Facial Recognition AI

02:24
Understanding Membership Inference Attacks

02:53
How Membership Inference Attacks Reveal People’s Identities in a Training Dataset

03:18
The Serious Impact of Membership Inference Attacks on People with HIV or Dementia

02:39
LAWGAMES: AI FORT KNOX PRO

Section 4: Black Box and White Box Attacks

Section 5: Addressing Adversarial Attacks

Section 6: Data Minimization Techniques

Five Key Techniques to Help You Achieve Data Minimization

04:00
Seven Key Steps to Balance AI Functionality and Data Minimization

03:20
Six Data Focused Techniques for a Privacy Conscious Approach to AI Development

03:43
The Importance of Integrating a Data Minimal Approach in Your AI And ML Design

02:22
Five Key Techniques for Data Minimalist Machine Learning

03:59
Four-Step Strategy to Data Minimalism When Training ML Systems

03:01
Five-Step Method to Strike a Balance Between Data Minimization and Statistical Accuracy

02:47
Three Crucial Data Minimalism Techniques at the ML Inference Stage

03:34
Worked Example: Statistical Accuracy and Data Minimization in Sales Behavior Prediction AI

02:13
LAWGAMES: AI FORT KNOX PRO

Section 7: Privacy-Enhancing Methods

Top Three Data Minimalist Privacy Enhancing Methods

02:59
Two Steps to the Application and Measurement of the Effectiveness of Privacy-Enhancing Techniques

02:46
The Significance and Challenges of Differential Privacy

03:11
Introduction to Perturbation and Controlled Randomness

02:22
Introduction to Noisy Words and Predictive Text Systems

02:55
The Impact of Perturbation on Individual-Level Accuracy

02:37
What are Privacy-Preserving Query Approaches and How Do They Work on ML Models?

03:07
Seven Key Steps to Effectively Managing Privacy Attacks on ML Models

03:23
LAWGAMES: AI FORT KNOX PRO

Section 8: Federated Learning and Synthetic Data

Section 9: Data Protection and Management

Section 10: Legal and Ethical Considerations

Section 11: Practical Applications and Implementation of ML Models

LAWGAMES: AI FORT KNOX PRO

Add this certificate to your resume to demonstrate your skills in AI law, tech, and integrity!

About Course

What Will You Learn?

Course Content

Section 1: AI Security Fundamentals

Seven Key Considerations When Assessing AI Systems’ Security

How to Inculcate an Understanding of Data Integrity Amongst Multidisciplinary AI Teams?

Four Key Factors That Impact AI Security

The Importance of Information Sharing Between ML Model Providers and Clients

The Hidden Dangers of ML Framework Dependencies

Recruitment Example of Vulnerability Exploit From Open-Source Programming Library

Three Key Measures to Safeguard Your ML Deployment Pipeline

LAWGAMES: AI FORT KNOX PRO

Section 2: Data Integrity and Attack Scenarios

Four AI Attack Scenarios That Compromise Data Integrity

Seven Steps to Secure Training Data in ML Systems

Three Techniques to Safeguard Sensitive Training Data in AI Systems

Six Key Considerations When Using External Software In Your AI

Benefits of Using Open Source Code Libraries In Your AI

Hidden Data Dangers of Support Vector Machines and K-Nearest Neighbors

De-Risking Healthcare Data In SVM or KNN Models

Personal Data Within SVM and KNN Models – is it an Attack Vector?

LAWGAMES: AI FORT KNOX PRO

Section 3: Model Inversion and Membership Inference Attacks

An Introduction to Model Inversion and Membership Inference Privacy Attacks

Understanding Model Inversion Attacks

Anatomy of an AI Model Inversion Attack in Healthcare

Anatomy of a Model Inversion Attack on Facial Recognition AI

Understanding Membership Inference Attacks

How Membership Inference Attacks Reveal People’s Identities in a Training Dataset

The Serious Impact of Membership Inference Attacks on People with HIV or Dementia

LAWGAMES: AI FORT KNOX PRO

Section 4: Black Box and White Box Attacks

Introduction to White Box and Black Box Model Attacks

Key Differences Between Black Box and White Box ML Attacks

Why You Should Withhold Confidence Scores To Avoid Black and White Box Attacks

How to Detect Privacy Attacks by Monitoring API User Queries

How to Address the Risk of White Box Attacks When Provisioning Full ML Models

LAWGAMES: AI FORT KNOX PRO

Section 5: Addressing Adversarial Attacks

Introduction to Adversarial Attacks

How Adversarial Attacks on ML Models Threaten People’s Legal Rights

How Adversarial Attacks Jeopardize Statistical Accuracy

Adversarial Attacks and Your Obligations Under the NIS Regulations 2018

Critical Trade-Off Between AI Explainability and Preserving Trade Secrets

LAWGAMES: AI FORT KNOX PRO

Section 6: Data Minimization Techniques

Five Key Techniques to Help You Achieve Data Minimization

Seven Key Steps to Balance AI Functionality and Data Minimization

Six Data Focused Techniques for a Privacy Conscious Approach to AI Development

The Importance of Integrating a Data Minimal Approach in Your AI And ML Design

Five Key Techniques for Data Minimalist Machine Learning

Four-Step Strategy to Data Minimalism When Training ML Systems

Five-Step Method to Strike a Balance Between Data Minimization and Statistical Accuracy

Three Crucial Data Minimalism Techniques at the ML Inference Stage

Worked Example: Statistical Accuracy and Data Minimization in Sales Behavior Prediction AI

LAWGAMES: AI FORT KNOX PRO

Section 7: Privacy-Enhancing Methods

Top Three Data Minimalist Privacy Enhancing Methods

Two Steps to the Application and Measurement of the Effectiveness of Privacy-Enhancing Techniques

The Significance and Challenges of Differential Privacy

Introduction to Perturbation and Controlled Randomness

Introduction to Noisy Words and Predictive Text Systems

The Impact of Perturbation on Individual-Level Accuracy

What are Privacy-Preserving Query Approaches and How Do They Work on ML Models?

Seven Key Steps to Effectively Managing Privacy Attacks on ML Models

LAWGAMES: AI FORT KNOX PRO

Section 8: Federated Learning and Synthetic Data

Practical (Healthcare) Applications of Federated Learning

Building a Banking Fraud Detection Model Through Federated Learning

Two Steps to Synthetic Data Generation

The Importance of Balancing Statistical Realism with Re-Identification Risks

LAWGAMES: AI FORT KNOX PRO

Section 9: Data Protection and Management

Data Protection Implications of Predictor and Target Variables

Snapshot of the Mechanisms of Prediction Algorithms

Snapshot of Training and Inference Phases Supervised Machine Learning

How to Approach Data Retention and Management in Machine Learning Lifecycle

Three Key Techniques to Address Overfitting in ML Models

LAWGAMES: AI FORT KNOX PRO

Section 10: Legal and Ethical Considerations