Privacy-Preserving Machine Learning E-Book

Srinivasa Rao Aravilli

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 494

Veröffentlichungsjahr: 2024

Beschreibung

– In an era of evolving privacy regulations, compliance is mandatory for every enterprise
– Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information
– This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases
– As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy
– Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models
– You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field
– Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks

Leseprobe

Privacy-Preserving Machine Learning

A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Srinivasa Rao Aravilli

Privacy-Preserving Machine Learning

Copyright © 2024 Packt Publishing

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To my mother, Jaya, and my father, Rama Sarma, for their sacrifices and for exemplifying the power of determination. To my wife, Uma Madhavi, for being my loving partner throughout our joint life journey, and my son, Atchuta Ram, and daughter, Akhila, for their love, support, and inspiration.

– Srinivasa Rao Aravilli

Foreword

In an era defined by an abundance of data and the transformative power of machine learning, the need to safeguard privacy and ensure the security of sensitive information has never been more critical.

In this groundbreaking book, Privacy-Preserving Machine Learning, Srinivasa Rao Aravilli presents a comprehensive exploration of the intersection between privacy and machine learning. Srinivasa, an esteemed expert in the field, offers a unique perspective on addressing the privacy challenges in machine learning and deep learning applications. The book takes us on a compelling journey into the world of privacy-preserving machine learning, providing a deep understanding of the underlying principles, methodologies, and cutting-edge techniques. Srinivasa elucidates the potential risks and vulnerabilities inherent in data pipelines, shedding light on the potential consequences of privacy breaches. Drawing on his extensive experience, he guides us through the intricate landscape of privacy preservation, offering invaluable insights and practical solutions.

He explores the latest techniques such as federated learning, federated learning benchmarks, homomorphic encryption, and differential privacy, providing a glimpse into the future of privacy-preserving machine learning on secure enclaves to protect data from insider threats. With the insights and methodologies presented in this book, readers will be empowered to strike the delicate balance between data utility and privacy preservation, fostering trust among individuals and organizations alike.

I commend Srinivasa for his meticulous research, practical insights using open source privacy-preserving ML and DL frameworks, and dedication to advancing the field of privacy-preserving machine learning.

This book serves as a valuable resource for data scientists, machine learning engineers, privacy professionals, and decision-makers seeking to unlock the transformative potential of machine learning while upholding the privacy rights of individuals.

Sam Hamilton

Head of Data and AI

Visa Inc.

Contributors

About the author

Srinivasa Rao Aravilli boasts 27 years of extensive experience in technology and leadership roles, spearheading innovation in various domains such as information retrieval, search, ML/AI, Generative AI, distributed computing, network analytics, privacy, and security. Currently serving as a senior director of machine learning engineering at Capital One, Bangalore, he has a proven track record of driving new products from conception to outstanding customer success. Prior to his tenure at Capital One, Srinivasa held prominent leadership positions at Visa, Cisco, and Hewlett Packard, where he led product groups focused on large-scale data, privacy, machine learning, and Generative AI. He holds a master’s degree in computer applications from Andhra University, Visakhapatnam, India.

I would like to thank the people who have been close to me and supported me, especially my family, parents, and my colleagues and team members.

About the reviewers

Dr. Nitin Agrawal is currently a privacy engineer at Snap Inc. Previously, he served as an applied scientist at Amazon in Alexa Privacy Science. Holding a doctoral degree from the University of Oxford, his research focuses on privacy-preserving machine learning and designing solutions for industry compliance with international privacy regulations, optimizing business utility. His work includes designing secure data-driven systems and advancing privacy protections through privacy-enhancing primitives including secure multi-party computation and homomorphic encryption. Recently, Dr. Agrawal has been actively involved in the privacy auditing of machine learning models, with a specific emphasis on privacy-aware Generative AI.

Dr. Agrawal acknowledges the unwavering support of his family, mentor, and colleagues in the production of this book.

Nandita Rao Narla is the head of technical privacy and governance at DoorDash. Previously, she was a founding team member of a data profiling startup and held various leadership roles at EY, where she helped Fortune 500 companies build and mature privacy, cybersecurity, and data governance programs. She is a Senior Fellow at Future of Privacy Forum and serves on the advisory boards and technical standards committees for IAPP, Ethical Tech Project, X Reality Safety Initiative, Institute of Operational Privacy Design, ISACA, and NIST. Nandita holds a BTech in computer science from JNT University, an MS in Information Security from Carnegie Mellon University, and certifications including FIP, CIPP/US, CIPT, CIPM, CDPSE, CISM, CRISC, and CISA.

Akshat Gurnani is a qualified professional in the field of computer science and machine learning, with a Master’s degree. His expertise covers a variety of machine learning techniques, including natural language processing, computer vision, and deep learning. Akshat’s significant contributions to academia are evident through his prolific publications in top-tier journals and conferences. His dedication to continuous learning ensures he remains at the forefront of the latest technological developments, seeking to drive forward advancements in artificial intelligence.

Table of Contents

Preface

Part 1: Introduction to Data Privacy and Machine Learning

1

Introduction to Data Privacy, Privacy Breaches, and Threat Modeling

What do privacy and data privacy mean?

Privacy regulations

Privacy by Design and a case study

Example – Privacy by Design in a social media platform

Privacy breaches

Equifax privacy breach

Clearview AI Privacy breach

Privacy threat modeling

Privacy threat modeling – definition

The importance of privacy threat modeling

Privacy threat modeling’s alignment to Privacy by Design principles

Steps in privacy threat modeling

Privacy threat modeling frameworks

The LINDDUN framework

Step 1 – modeling the system

Step 2 – eliciting and documenting threats

Step 3 – mitigating threats

The need for privacy-preserving ML

Case study – privacy-preserving ML in financial institutions

Summary

2

Machine Learning Phases and Privacy Threats/Attacks in Each Phase

ML types

Supervised ML

Unsupervised ML

Reinforced ML

Overview of ML phases

The main phases of ML

Privacy threats/attacks in ML phases

Collaborative roles in ML projects

Privacy threats/attacks in ML

Membership inference attack

Model extraction attack

Reconstruction attacks—model inversion attacks

Model inversion attacks in neural networks

Summary

Part 2: Use Cases of Privacy-Preserving Machine Learning and a Deep Dive into Differential Privacy

3

Overview of Privacy-Preserving Data Analysis and an Introduction to Differential Privacy

Privacy in data analysis

The need for privacy in data analysis

Privacy-preserving techniques

Data anonymization and algorithms for data anonymization

Data aggregation

Privacy-enhancing technologies

Differential privacy

Federated learning

Secure multi-party computation (SMC)

Homomorphic encryption

Anonymization

De-identification

Differential privacy

Summary

4

Overview of Differential Privacy Algorithms and Applications of Differential Privacy

Differential privacy algorithms

Laplace distribution

Gaussian distribution

Comparison of noise-adding algorithms to apply differential privacy

Generating aggregates using differential privacy

Sensitivity

Queries that use differential privacy

Clipping

Overview of real-life applications of differential privacy

Differential privacy usage at Uber

Differential privacy usage at Apple

Differential privacy usage in the US Census

Differential privacy at Google

Summary

5

Developing Applications with Differential Privacy Using Open Source Frameworks

Open source frameworks to implement differential privacy

Introduction to the PyDP framework and its key features

Examples and demonstrations of PyDP in action

Developing a sample banking application with PyDP to showcase differential privacy techniques

Protecting against membership inference attacks

Applying differential privacy to large datasets

Use case – generating differentially private aggregates on a large dataset

PipelineDP high-level architecture

Tumult Analytics

Machine learning using differential privacy

Synthetic Dataset Generation: Introducing Fraudulent Transactions

Develop a classification model using scikit-learn

High-level implementation of the SGD algorithm

Applying differential privacy options using machine learning

Generating gradients using differential privacy

Clustering using differential privacy

Deep learning using differential privacy

Fraud detection model using PyTorch

Fraud detection model with differential privacy using the Opacus framework

Differential privacy machine learning frameworks

Limitations of differential privacy and strategies to overcome them

Summary

Part 3: Hands-On Federated Learning

6

Federated Learning and Implementing FL Using Open Source Frameworks

Federated learning

Preserving privacy

FL definition

Characteristics of FL

FL algorithms

FedSGD

FedAvg

Fed Adaptative Optimization

The steps involved in implementing FL

Open source frameworks to implement FL

TensorFlow Federated

Flower

An end-to-end use case of implementing fraud detection using FL

Developing an FL model for fraud detection using the Flower framework

FL with differential privacy

Approach one

Approach two

A sample application using FL-DP

Summary

7

Federated Learning Benchmarks, Start-Ups, and the Next Opportunity

FL benchmarks

The importance of FL benchmarks

FL datasets

Frameworks for FL benchmarks

Selecting an FL framework for a project

A comparison of FedScale, FATE, Flower, and TensorFlow Federated

State-of-the-art research in FL

Communication-efficient FL

Privacy-preserving FL

Federated Meta-Learning

Adaptive FL

Federated reinforcement learning

Key company products related to FL

Summary

Part 4: Homomorphic Encryption, SMC, Confidential Computing, and LLMs

8

Homomorphic Encryption and Secure Multiparty Computation

Encryption, anonymization, and de-identification

Data anonymization

De-identification

Exploring Homomorphic encryption

Ring-based

Lattice-based

Elliptic curve-based

Exploring the mathematics behind HE

Encryption

Homomorphism

Types of HE

Fully Homomorphic Encryption (FHE)

Somewhat Homomorphic Encryption (SHE)

Partially Homomorphic Encryption (PHE)

Paillier scheme

Pyfhel

SEAL Python

TenSEAL

phe

Implementing HE

Implementing PHE

Implementing HE using the TenSEAL library

Comparison of HE frameworks

Pyfhel

TenSEAL

PALISADE

PySEAL

TFHE

Machine learning with HE

Encrypted evaluation of ML models and inference

Limitations of HE

Secure Multiparty Computation

Basic principles of SMC

Applications of SMC

Techniques used for SMC

Implementing SMC – high-level steps

Python frameworks that can be used to implement SMC

Implementing Private Set Interaction (PSI) SMC – case study

Zero-knowledge proofs

Basic concepts

Types of ZKPs

Applications of ZKPs

Summary

9

Confidential Computing – What, Why, and the Current State

Privacy/security attacks on data in memory

Data at rest

Data in motion

Data in memory

Confidential computation

What is confidential computing?

Benefits of confidential computing

Trusted execution environments – attestation of source code and how it helps protect against insider threat attacks

Industry standards for ML in TEEs

Confidential Computing Consortium

High-level comparison of Intel SGX, AWS Nitro Enclaves, Google Asylo, Azure enclaves, and Anjuna

Pros and cons of TEEs

Summary

10

Preserving Privacy in Large Language Models

Key concepts/terms used in LLMs

Prompt example using ChatGPT (closed source LLM)

Prompt example using open source LLMs

Comparison of open source LLMs and closed source LLMs

AI standards and terminology of attacks

NIST

OWASP Top 10 for LLM applications

Privacy attacks on LLMs

Membership inference attacks against generative models

Extracting training data attack from generative models

Prompt injection attacks

Privacy-preserving technologies for LLMs

Text attacks on ML models and LLMs

Private transformers – training LLMs using differential privacy

STOA – Privacy-preserving technologies for LLMs

Summary

Index

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Part 1: Introduction to Data Privacy and Machine Learning

This part provides an introduction to the fundamental concepts of data privacy and the distinction between sensitive data and personal sensitive data, along with the importance of data privacy regulations. The concept of privacy by design is discussed, emphasizing the proactive integration of privacy measures into systems and processes. Additionally, notable privacy breaches in major enterprise companies are examined, highlighting the potential consequences and risks associated with such incidents. This introduction sets the foundation for understanding the significance of data privacy and the need for robust privacy measures. This part also covers privacy threat modeling using the LINDDUN framework in detail.

The second chapter in this part focuses on the different phases of the machine learning pipeline and the privacy threats and attacks that can occur at each stage. We will explore the phases of data collection, data preprocessing, model training, and inference. Within each phase, specific privacy threats and attacks, such as model inversion attacks and training data extraction attacks, are discussed in detail, providing illustrative examples. The importance of protecting training data privacy, input data privacy, model privacy, and inference/output data privacy is emphasized. This part highlights the potential risks and challenges associated with privacy in machine learning, underlining the need for robust privacy preservation techniques throughout the entire process. Exploration of privacy threats and attacks in each phase of the machine learning pipeline sheds light on the challenges of preserving privacy in machine learning systems.

This part has the following chapters:

Chapter 1, Introduction to Data Privacy, Privacy Breaches, and Threat Modeling Chapter 2, Machine Learning Phases and Privacy Threats/Attacks in Each Phase

1

Introduction to Data Privacy, Privacy Breaches, and Threat Modeling

Privacy-preserving machine learning (ML) is becoming increasingly important in today’s digital age, where the use of personal data is ubiquitous in various industries, including healthcare, finance, and marketing. While ML can bring many benefits, such as improved accuracy and efficiency, it also raises significant concerns about privacy and security. Many individuals are increasingly concerned about the risks associated with the use of their personal data, including unauthorized access, misuse, and abuse. Furthermore, there are regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) that require organizations to comply with strict privacy guidelines while processing personal data.

This book provides a comprehensive understanding of the techniques and tools available to protect individuals’ privacy while enabling effective ML. This book will help researchers, ML engineers, software engineers, and practitioners to understand the importance of privacy and how to incorporate it into their ML algorithms and data processing pipelines. This book bridges the gap between the theoretical foundations of privacy and the practical implementation of privacy-preserving ML techniques, enabling data-driven decision-making without compromising individuals’ privacy.

In this introductory chapter, we are going to learn about privacy, including data privacy; sensitive data versus personal sensitive data; data privacy regulations; Privacy by Design (PbD) concepts; and why data privacy is important. Once we have discussed these concepts, we will cover privacy threat modeling using the LINDDUN framework in detail and explain linkability and identifiability threats with an example. This chapter will help you to better understand privacy and why it is important. We will discuss key privacy regulations, such as the GDPR and CPRA, at a high level, as well as privacy threat modeling. At the end of this chapter, we will cover the need for privacy-preserving ML and a use case.

We will cover the following main topics:

What do privacy and data privacy mean?Privacy by Design and a case studyPrivacy breachesPrivacy threat modelingThe need for privacy-preserving ML

What do privacy and data privacy mean?

Alan Westin’s theory describes privacy as the control over how information about a person is handled and communicated to others. Irwin Altman added that privacy includes limiting social interaction and included regulating personal space and territory.

Personal data includes any information that by itself or in conjunction with other elements can be used to identify an individual, such as their name, age, gender, personal identification number, race, religion, address, email address, biometric data, device IDs, medical data, and genetics data, based on the regulations defined in the country where the orginated from.

Privacy refers to an individual’s ability to keep their information, whether it is personal or non-personal data, to themselves and share data based on their consent. Privacy helps individuals maintain autonomy over their personal lives.

Data privacy focuses on the use and governance of personal data and policies to ensure that data is collected, processed, shared, and used/inferred in an appropriate way.

Privacy regulations

As per the latest statistics of the United Nations Conference on Trade and Development (UNCTAD), 71% of countries have their own privacy laws, which shows the importance of privacy and data protection across the world.

Most privacy laws deal with the collection of sensitive personal data, data processing, sharing data with other parties, and data subject rights. 137 out of 194 countries in the world have legal legislation to protect data and individuals’ data privacy.

Figure 1.1 – Privacy legislation worldwide as of December 2021

Source: https://unctad.org/page/data-protection-and-privacy-legislation-worldwide

Out of these privacy regulations across the world, the most popular and widely implemented ones are the GDPR in Europe and the CCPA in the US.

As per the GDPR, personal data is defined as follows:

The data subjects are identifiable if they can be directly or indirectly identified, especially by reference to an identifier such as a name, an identification number, location data, an online identifier or one of several special characteristics, which expresses the physical, physiological, genetic, mental, commercial, cultural or social identity of these natural persons. In practice, these also include all data which are or can be assigned to a person in any kind of way. For example, the telephone, credit card or personnel number of a person, account data, number plate, appearance, customer number or address are all personal data.

In this definition, the keywords are whether the person can be identified either directly or indirectly using an identifier mentioned as name. We will learn more about indirect identification, how individuals can be identified through indirect identifiers, and how their privacy is compromised in the Privacy threat modeling section.

The GDPR also defines sensitive personal data, which includes genetic, biometric, and health data, as well as personal data revealing racial and ethnic origin, political opinions, religious or ideological convictions, or trade union membership. Most regulations have articles/sections covering the following when working with personal data, non-personal data, and sensitive personal data:

Purpose, scope, and definition of personal data: The purpose of the privacy law, specifying its scope and outlining the types of data and entities covered by the law. It clarifies the legal framework’s intent and applicability and definitions of terms such as personal data and sensitive personal data.Privacy enforcement authority: Regulations define the role of the data protection authority or supervisory body responsible for overseeing compliance with the law, providing guidance, and handling complaints.Fines and penalties: Most laws have different fines/penalties based on the nature of privacy violations in that country. For example, the GDPR imposes a fine of 20 million euros, or up to 4% of the total global turnover of the preceding fiscal year of the company, whichever is higher for severe violations.Rights: Some countries define privacy as a fundamental right of people in that country. Each individual has rights to their data, that is, the right to know, remove, forget, and delete data.

The following table lists the data subject rights defined by popular privacy laws across the world:

Privacy Laws

Data Subject Rights

GDPR

Right to be informed

Right to access

Right to rectification

Right to erasure/be forgotten

Right to data portability

Right to restrict data processing

Right to withdraw consent

Right to object processing

Right to object automated decision-making

CCPA

Right to know

Right to access

Right to delete

Right to opt-out

Right to non-discrimination

Right to correct

Right to limit

LGPD

(This is Brazil’s General Personal Data Protection Law – Lei Geral de Proteção de Dados (LGPD))

Right to be informed

Right to access

Right to rectification

Right to erasure

Right to data portability

Right to object processing

Right to object automated decision-making

Table 1.1 – Data subject rights

We have gone through a high-level definition of privacy, privacy regulations in various countries, and data subject requests (rights of individuals).

Let’s now learn more about PbD, what it is, and how it helps to protect data privacy.

Privacy by Design and a case study

The concept of PbD was created by Ann Cavoukian in the 1990s and presented in her 2009 presentation, “Privacy by Design: The Definitive Workshop.” As Cavoukian states, the concept of PbD encompasses more than just technology.

PbD is a framework that promotes the integration of privacy and data protection principles into the design and development of systems, products, and services.

The PbD framework has seven foundational principles. The objective of these principles is to ensure that privacy is embedded in every stage of a system’s development and that data subjects’ privacy rights are protected:

Proactive not reactive measures: PbD requires that privacy considerations be integrated into the design and development of a system from the outset, rather than being added as an afterthought.Privacy as the default setting: PbD requires that privacy settings be set to the highest level by default and that users must opt-in to more invasive settings.End-to-end security: PbD requires that privacy and security measures be integrated throughout the entire life cycle of a system, from design and development to deployment and decommissioning. I strongly suggest using a “begin with privacy” approach instead of shift-left privacy. In this way, privacy begins from the software requirements phase itself.Full functionality: PbD requires that privacy and data protection measures be integrated in a way that does not compromise the functionality of the system or product.Visibility and transparency: PbD requires that users are informed of the privacy risks associated with a system or product and that they have access to information about how their data is being collected, used, and shared.Respect for user privacy: PbD requires that users have control over their personal data and that their privacy preferences are respected.Holistic approach: PbD requires that privacy and data protection considerations are integrated into all aspects of a system or product, including its technical design, operational procedures, and business practices.

The PbD approach has become increasingly important in recent years, as privacy concerns have grown in response to the rapid expansion of data-driven technologies.

PbD is now widely recognized as a best practice for organizations that process personal data and is an important component of data protection regulations, such as the EU’s GDPR.

Overall, PbD is a comprehensive framework that aims to ensure that privacy is an integral part of any system or product and that data protection is considered from the beginning of the development process, rather than as an afterthought.

Let’s walk through an example to understand PbD in detail.

Example – Privacy by Design in a social media platform

PbD is a framework that advocates for embedding privacy considerations into the design and architecture of systems, products, and services from the very beginning. By incorporating privacy as a core component, organizations can proactively address privacy concerns and ensure the protection of user data. This example case study illustrates how a social media platform can implement PbD principles.

Case study description

Let’s consider a hypothetical social media platform called “MyConnect,” which aims to prioritize user privacy and data protection by implementing PbD principles throughout its development and operation. We will explore its principles one by one.

Minimized data collection: MyConnect follows a privacy-focused approach by collecting only necessary user data. It only requests information that is directly relevant to providing the platform’s core functionality. Unnecessary data points, such as excessive personal details or invasive tracking information, are deliberately avoided.Privacy-oriented default settings: MyConnect implements privacy-oriented settings to protect user privacy by default. For example, it sets user profiles to private, limiting the visibility of user information to only approved connections. Additionally, it enables opt-in consent for features such as location sharing, ensuring that users have to actively choose to share their location.Granular privacy controls: MyConnect offers granular privacy controls, empowering users to manage their privacy preferences. Users have control over who can view their posts, access their profile information, and send connection requests. The platform provides easy-to-use privacy settings that allow users to customize their privacy levels according to their preferences.Secure data storage and encryption: MyConnect prioritizes the security of user data by employing strong encryption mechanisms. User data, including personal information and communications, is stored securely and encrypted both at rest and during transmission. This ensures that even if a data breach occurs, the data remains unreadable and protected.Regular security audits and updates: MyConnect conducts regular security audits to identify potential vulnerabilities and address them promptly. It stays updated with the latest security measures and patches any identified security weaknesses to ensure the ongoing protection of user data.Transparency and user education: MyConnect maintains transparency with its users by providing clear and concise privacy policies and terms of service. It educates users about their rights, the data collected, and how it is used. The platform also offers user-friendly guides and resources to educate users about privacy best practices and how to protect their information. MyConnect also implements a new way of sharing the details of how it protects data privacy on its platform through “privacy data sheets.”

The following mapping shows how the PbD principle is implemented at the social media platform company:

Privacy By Design – Principle

MyConnect Implementation

Proactive not reactive measures

Minimized data collection

Privacy as the default setting

Privacy-oriented default settings

Respect for user privacy

Granular privacy controls

End-to-end security

Secure data storage and encryption

Holistic approach

Regular security audits and updates

Visibility and transparency

Transparency and user education

Table 1.2 - MyConnect implementation

Benefits and outcomes

Implementing PbD principles in MyConnect yields several key benefits:

Enhanced usertrust: Users of MyConnect feel confident that their privacy is respected and their data is protected. The platform’s commitment to privacy empowers users to engage and share content without undue concerns about their personal information being misused.Compliance with privacy regulations: By incorporating PbD principles, MyConnect ensures compliance with privacy regulations, such as the GDPR. This protects the platform from legal and reputational risks associated with privacy breaches.Positive reputation and differentiation: MyConnect gains a competitive advantage by promoting itself as a privacy-conscious social media platform. Its PbD approach can attract privacy-conscious users who prioritize the protection of their personal information.Reduced privacy incidents and breaches: PbD practices reduce the likelihood of privacy incidents and data breaches. By incorporating privacy considerations from the start of the project, MyConnect minimizes the potential vulnerabilities that could lead to unauthorized access or misuse of user data. We will go through privacy breaches in more detail in the next section.

MyConnect’s implementation of PbD principles showcases the significance of considering privacy as a fundamental component in the design and operation of a social media platform by prioritizing minimized data collection, privacy-oriented defaults, granular privacy controls, secure data storage, regular audits, transparency, and user education.

Privacy breaches

What is a privacy breach?

A privacy breach, also known as a data breach, refers to an incident where unauthorized individuals or entities gain access to confidential or sensitive information without proper authorization. This breach of privacy can occur in various forms, such as hacking, theft, accidental exposure, or improper handling of data. It typically involves the unauthorized access, acquisition, disclosure, or use of personal information, which may include personally identifiable information (PII) such as names, addresses, social security numbers, financial details, and login credentials.

Privacy breaches can have serious consequences for individuals, organizations, and even society. They can lead to identity theft, financial fraud, reputational damage, loss of trust, legal implications, and emotional distress for those affected. Protecting personal data and maintaining privacy is essential to ensure the security and well-being of individuals and maintain trust in digital systems and services.

The following are some examples of privacy breaches: one involves a company utilizing web technologies in their product, while the other concerns a company incorporating artificial intelligence (AI) and ML into their products and services.

Equifax privacy breach

The Equifax privacy breach refers to a massive data breach that occurred in 2017, in which the personal information of approximately 147 million people was compromised. Equifax is one of the largest consumer credit reporting agencies in the United States, and the breach was one of the most significant data breaches in history.

The breach occurred when hackers exploited a vulnerability in Equifax’s website software, allowing them to gain access to sensitive information such as names, social security numbers, birth dates, addresses, and, in some cases, driver’s license numbers and credit card information.

The breach went undetected for several months, during which time the hackers were able to access and steal the information. The Equifax breach was a significant event, and it highlighted the importance of cybersecurity and the need for companies to take proactive measures to protect their customers’ data.

The breach also resulted in numerous investigations, lawsuits, and settlements against Equifax, with the company ultimately agreeing to pay over $700 million in damages and penalties. In addition to the financial impact, the breach had serious consequences for the affected individuals, who were at risk of identity theft and other fraudulent activities. The breach highlighted the need for individuals to be vigilant about monitoring their credit reports, protecting their personal information, and taking steps to protect themselves from identity theft.

The attackers used a combination of techniques, including SQL injection and cross-site scripting (XSS), to gain access to sensitive data stored in Equifax’s databases. SQL injection is a type of attack in which an attacker injects malicious code into a SQL statement, allowing them to execute unauthorized actions on a database. In this case, the attackers used SQL injection to bypass Equifax’s security controls and gain access to the personal information of millions of individuals.

The attackers also used XSS attacks, which involve injecting malicious code into a website to steal sensitive data from users. In this case, the attackers were able to inject malicious code into Equifax’s website. Once the attackers gained access to Equifax’s systems, they were able to extract large amounts of data over a period of several months without being detected. The data stolen included names, social security numbers, birth dates, addresses, driver’s license numbers, and credit card information.

The Equifax breach highlights the importance of cybersecurity and the need for companies to take proactive measures to protect their systems and data from attackers. It also underscores the importance of ongoing monitoring and detection to quickly identify and respond to potential security threats.

Source: https://en.wikipedia.org/wiki/2017_Equifax_data_breach

Clearview AI Privacy breach

Clearview AI is a technology company that developed a controversial facial recognition system. The company’s software was designed to match images of individuals with publicly available photos, scraping data from various sources on the internet, including social media platforms. The system gained widespread attention due to concerns over privacy and ethical implications.

In early 2020, Clearview AI found itself at the center of a major privacy breach. It was revealed that the company had amassed a massive database of billions of facial images without the knowledge or consent of the individuals involved. These images were collected from various online platforms, including Facebook, Instagram, and X (formerly Twitter).

The breach was brought to light by investigative reports and researchers who discovered that Clearview AI’s database was accessible to law enforcement agencies and other organizations. It raised significant concerns about the potential misuse of the technology, as it could be employed for mass surveillance, tracking individuals, or invading people’s privacy without their knowledge.

One of the primary concerns regarding Clearview AI’s practices was the lack of transparency and consent. Individuals whose photos were included in the database had not given their permission or even been aware that their images were being used in this manner. Clearview AI’s scraping of publicly available data bypassed many social media platforms’ terms of service, further exacerbating the privacy issues.

The breach prompted legal and ethical debates about the use of facial recognition technology and the need for stronger regulations. Critics argued that Clearview AI’s practices were an invasion of privacy, as people’s faces were being used as biometric identifiers without their consent.

Additionally, there were concerns about the potential for racial bias and discrimination in the system, as facial recognition algorithms have shown to be less accurate for certain demographics. Following the revelation of the privacy breach, Clearview AI faced significant backlash from privacy advocates, technology experts, and the public. Several lawsuits were filed against the company, accusing it of violating privacy laws and regulations. As a result, Clearview AI was subject to investigations by various regulatory authorities.

In response to the backlash, Clearview AI made efforts to improve its practices and address privacy concerns. The company claimed to have implemented stricter policies regarding data access and established a verification system for potential clients. However, skepticism remains regarding the efficacy of these measures and the overall ethics of the company’s operations.

The Clearview AI privacy breach serves as a cautionary tale about the potential dangers of unchecked facial recognition technology and the importance of safeguarding personal privacy. It has fueled discussions surrounding privacy laws, regulation of emerging technologies, and the ethical implications of mass surveillance. As the debate continues, it remains crucial to strike a balance between technological advancement and protecting individuals’ rights and privacy.

Privacy threat modeling

In an increasingly digital world, privacy has become a paramount concern for individuals, organizations, and societies at large. With the widespread collection and processing of personal data, it is essential to assess and mitigate privacy threats effectively.

Privacy threat modeling – definition

Privacy threat modeling is a proactive process that aims to identify and understand potential threats to privacy before they materialize. By examining the system’s architecture, data flows, and interactions, privacy threat modeling allows for the identification of vulnerabilities and risks that may compromise individuals’ privacy. It helps organizations anticipate and address privacy concerns during the design and development stages, ensuring privacy protections are integrated into the system from the outset.

The importance of privacy threat modeling

Privacy threat modeling offers several key benefits, including the following:

Risk identification: By systematically assessing potential privacy threats, organizations can identify and understand the risks they face. This knowledge enables them to prioritize privacy controls and allocate resources effectively.PbD approach: Privacy threat modeling encourages a privacy-centric approach to system design and development. By integrating privacy considerations early on, organizations can save time, effort, and costs that may otherwise be required for retrofitting privacy safeguards.Compliance and accountability: Privacy regulations and standards, such as the GDPR, require organizations to implement privacy measures. Privacy threat modeling helps organizations fulfill these requirements by identifying and addressing potential compliance gaps.Stakeholder trust: Demonstrating a commitment to privacy protection enhances stakeholder trust. Privacy threat modeling provides a systematic way to showcase an organization’s dedication to safeguarding individuals’ privacy, leading to increased confidence among users (internal and external) and customers. It builds a culture of responsible development.

Continuous privacy threat modeling helps clarify the requirements for privacy and allows organizations to move toward building standard privacy features and patterns. Focusing on proactive issue identification and fixing helps companies build a privacy-forward culture.

Privacy threat modeling’s alignment to Privacy by Design principles

Privacy threat modeling involves identifying potential privacy risks and vulnerabilities within a system. While it doesn’t directly encompass all PbD principles, it is an essential step in implementing those principles effectively.

Here’s how privacy threat modeling aligns with different PbD principles:

Data minimization: Helps identify areas where data collection might be excessive or unnecessary, leading to potential privacy risksPurpose specification: Identifies scenarios where collected data might be used for unintended purposes, helping to ensure that data use is appropriately specifiedConsent mechanisms: Highlights instances where data might be collected or used without proper user consent, assisting in designing effective consent processesAccess controls: Identifies potential unauthorized access points, guiding the implementation of access controls to prevent unauthorized data exposureData encryption: Reveals vulnerabilities in data storage or transmission that could lead to data breaches, informing the need for encryptionUser empowerment: Helps identify areas where users might lack control over their data, prompting the implementation of tools for user data managementSecurity measures: Identifies potential security weaknesses that could compromise user data, contributing to the implementation of robust security measuresRegular audits and assessments: Supports ongoing assessments by identifying areas of potential vulnerability that require regular monitoring and evaluation

While privacy threat modeling is not a direct substitute for PbD principles, it plays a crucial role in shaping the design and development process by identifying potential risks and vulnerabilities. The insights gained from threat modeling enable organizations to effectively apply PbD principles to address those risks and enhance the overall privacy posture of their systems.

Steps in privacy threat modeling

Performing an effective privacy threat assessment involves the following steps:

Define the system: Clearly define the scope of the system or application under assessment. Identify its components, data flows, and interfaces with other systems.Identify data types: Determine the types of personal data the system processes and stores. Categorize the data based on sensitivity and regulatory requirements.Identify threat sources: Enumerate potential threat sources, both internal and external, that may attempt to compromise the privacy of the system’s data or users.Analyze threat scenarios: Develop realistic threat scenarios by combining threat sources and system components. Consider scenarios that may exploit vulnerabilities in data handling, storage, transmission, or user interactions.Assess impact and likelihood: Evaluate the potential impact and likelihood of each threat scenario materializing. Consider the potential harm to individuals, regulatory penalties, reputation damage, and other relevant factors.Identify controls: Identify and implement appropriate privacy controls and safeguards to mitigate identified threats. Consider technical, organizational, and procedural measures to address vulnerabilities and protect privacy.Document and communicate: Document the privacy threat assessment process, including identified threats, mitigations, and residual risks. Communicate the findings to stakeholders, such as system designers, developers, privacy officers, and management, to ensure collective awareness and buy-in.Review and update: Regularly review and update the privacy threat assessment as the system evolves or new threats emerge.

Privacy threat modeling is an iterative process that should be integrated into the organization’s ongoing privacy management practices

Privacy threat modeling frameworks

There are several privacy threat modeling frameworks available that provide structured methodologies and guidelines to assess and mitigate privacy risks effectively. Let’s explore some of the widely recognized privacy threat modeling frameworks:

STRIDE (Microsoft): The STRIDE framework, originally developed by Microsoft, focuses on identifying threats to the security and privacy of a system. It stands for the following:Spoofing identity: Unauthorized actors masquerade as legitimate usersTampering with data: Unauthorized modification or destruction of dataRepudiation: Denial of actions or transactions by malicious actorsInformation disclosure: Unauthorized access to sensitive informationDenial of service: Disruption or degradation of system availabilityElevation of privilege: Unauthorized escalation of privileges

The STRIDE framework helps identify potential privacy threats by considering how each threat category could impact the privacy of the system’s users and data.

LINDDUN: This is a privacy threat modeling framework. It provides a comprehensive approach to identify and address privacy concerns. The components of the LINDDUN framework are as follows:Linkability: Assessing the potential for linking various data points to identify individualsIdentifiability: Evaluating the extent to which individuals can be identified or re-identified from the dataNon-repudiation: Ensuring that actions and transactions cannot be deniedDetectability: Assessing the ability to detect privacy breaches or unauthorized accessData disclosure: Excessively collecting, storing, processing, or sharing personal dataUnawareness: Evaluating the level of user awareness and control over data collection and usageNon-compliance: Identifying risks of non-compliance with privacy regulations and standards

LINDDUN provides a holistic view of privacy threats and helps organizations analyze the impact of these threats on individuals’ privacy.

PLOT4AI: The Privacy Library of Threats for Artificial Intelligence (PLOT4AI) is a comprehensive resource meticulously crafted to tackle the intricate web of privacy concerns entwined with AI technology. Presently, the library comprises an assemblage of 86 unique threats, meticulously categorized into eight distinct groupings:Techniques and processes: This category covers the potential downsides stemming from processes or technical maneuvers capable of detrimentally affecting individualsAccessibility: This aims to rectify the deficiency in the accessibility and user-friendliness of AI systems for a diverse range of individualsIdentifiability and linkability: This casts a spotlight on threats and linking individuals to specific attributes or other individuals, coupled with apprehensions surrounding identificationSecurity: This zooms in on the potential perils arising from inadequately fortified AI systems and procedures against security vulnerabilitiesSafety: This concentrates efforts on recognizing perils and shielding individuals from plausible harm or jeopardyUnawareness: This confronts the issue of neglecting to inform individuals and extends to them the chance to interveneEthics and human rights: This illuminates conceivable adverse effects on individuals or the harm borne out of an absence of consideration for values and principlesNon-compliance: This directs attention toward threats emerging from the failure to adhere to data protection laws and other pertinent regulations

This repository stands as a robust arsenal to empower the AI community and stakeholders in safeguarding the paramount importance of privacy as AI continues to unfold its potential.

The library introduces a simplified four-phase development life cycle (DLC) approach, aligning with various methodologies, such as SEMMA, CRISP-DM, ASUM-DM, TDSP, and MDM. This streamlined approach ensures accessibility for non-technical stakeholders while maintaining alignment with established methodologies.

Data flow diagrams (DFDs) are employed as visual representations of systems under analysis. PLOT4AI emphasizes the importance of thorough threat modeling and suggests using both basic and detailed DFDs for different categories of threats.

Threats are presented in the form of cards, similar to LINDDUN GO, categorized by colors and icons representing the threat category and DLC phase. Some threats might have multiple category icons or DLC icons, reflecting their diverse impacts.

To practically apply PLOT4AI, it can be used as a card game in both physical and digital formats. Sessions are timeboxed to maintain engagement and focus, involving diverse stakeholders and a facilitator. In the sessions, participants are guided through each threat card’s question, discussion, and potential recommendations.

By utilizing PLOT4AI, organizations can enhance their privacy practices, mitigate risks, streamline processes, and foster collaboration among stakeholders. The library’s output can also contribute to data privacy impact assessments and facilitate compliance efforts.

While still in development, PLOT4AI offers benefits such as improved processes, reduced rework, clearer purpose, and alignment among stakeholders. The resource provides valuable insights into humanizing AI through the lens of privacy threat modeling.

The company provides an online assessment tool that, through responding to its queries, enables individuals to discern the threat model pertinent to the ML systems or