grouping definitions

contents/core/privacy_security/privacy_security.qmd

@@ -10,7 +10,7 @@ Resources: [Slides](#sec-security-and-privacy-resource),  [Videos](#sec-security
 
 ![_DALL·E 3 Prompt: An illustration on privacy and security in machine learning systems. The image shows a digital landscape with a network of interconnected nodes and data streams, symbolizing machine learning algorithms. In the foreground, there's a large lock superimposed over the network, representing privacy and security. The lock is semi-transparent, allowing the underlying network to be partially visible. The background features binary code and digital encryption symbols, emphasizing the theme of cybersecurity. The color scheme is a mix of blues, greens, and grays, suggesting a high-tech, digital environment._](images/png/cover_security_privacy.png)
 
-Security and privacy are critical when developing real-world machine learning systems. As machine learning is increasingly applied to sensitive domains like healthcare, finance, and personal data, protecting confidentiality and preventing misuse of data and models becomes imperative. Anyone aiming to build robust and responsible ML systems must grasp potential security and privacy risks such as data leaks, model theft, adversarial attacks, bias, and unintended access to private information. We also need to understand best practices for mitigating these risks. Most importantly, security and privacy cannot be an afterthought and must be proactively addressed throughout the ML system development lifecycle - from data collection and labeling to model training, evaluation, and deployment. Embedding security and privacy considerations into each stage of building, deploying, and managing machine learning systems is essential for safely unlocking the benefits of A.I.
+Security and privacy are critical when developing real-world machine learning systems. As machine learning is increasingly applied to sensitive domains like healthcare, finance, and personal data, protecting confidentiality and preventing misuse of data and models becomes imperative. Anyone aiming to build robust and responsible ML systems must grasp potential security and privacy risks such as data leaks, model theft, adversarial attacks, bias, and unintended access to private information. We also need to understand best practices for mitigating these risks. Most importantly, security and privacy cannot be an afterthought and must be proactively addressed throughout the ML system development lifecycle - from data collection and labeling to model training, evaluation, and deployment. Embedding security and privacy considerations into each stage of building, deploying, and managing machine learning systems is essential for safely unlocking the benefits of AI.
 
 ::: {.callout-tip}
 
@@ -40,13 +40,7 @@ Security and privacy are critical when developing real-world machine learning sy
 
 Machine learning has evolved substantially from its academic origins, where privacy was not a primary concern. As ML migrated into commercial and consumer applications, the data became more sensitive - encompassing personal information like communications, purchases, and health data. This explosion of data availability fueled rapid advancements in ML capabilities. However, it also exposed new privacy risks, as demonstrated by incidents like the [AOL data leak in 2006](https://en.wikipedia.org/wiki/AOL_search_log_release) and the [Cambridge Analytica](https://www.nytimes.com/2018/04/04/us/politics/cambridge-analytica-scandal-fallout.html) scandal.
 
-These events highlighted the growing need to address privacy in ML systems. In this chapter, we explore privacy and security considerations together, as they are inherently linked in ML:
-
-* Privacy refers to controlling access to sensitive user data, such as financial information or biometric data collected by an ML application.
-
-* Security protects ML systems and data from hacking, theft, and misuse.
-
-For example, an ML-powered home security camera must secure video feeds against unauthorized access and provide privacy protections to ensure only intended users can view the footage. A breach of either security or privacy could expose private user moments.
+These events highlighted the growing need to address privacy in ML systems. In this chapter, we explore privacy and security considerations together, as they are inherently linked in ML. For example, an ML-powered home security camera must secure video feeds against unauthorized access and provide privacy protections to ensure only intended users can view the footage. A breach of either security or privacy could expose private user moments.
 
 Embedded ML systems like smart assistants and wearables are ubiquitous and process intimate user data. However, their computational constraints often prevent heavy security protocols. Designers must balance performance needs with rigorous security and privacy standards tailored to embedded hardware limitations.
 
@@ -56,15 +50,15 @@ We hope that by building a holistic understanding of risks and safeguards, you w
 
 ## Terminology
 
-In this chapter, we will discuss security and privacy together, so there are key terms that we need to be clear about.
+In this chapter, we will discuss security and privacy together, so there are key terms that we need to be clear about. Since these terms are general concepts applied in many domains, we want to define how they relate to the context of this chapter and provide relevant examples to illustrate their application.
 
-* **Privacy:** Consider an ML-powered home security camera that identifies and records potential threats. This camera records identifiable information of individuals approaching and potentially entering this home, including faces. Privacy concerns may surround who can access this data.
+ * **Privacy:** The ability to control access to sensitive user data collected and processed by a system. In machine learning, this involves ensuring that personal information, such as financial details or biometric data, is accessible only to authorized individuals. For instance, a home security camera powered by machine learning might record video footage and identify faces of visitors. Privacy concerns center on who can access, view, or share this sensitive data.
 
-* **Security:** Consider an ML-powered home security camera that identifies and records potential threats. The security aspect would ensure that hackers cannot access these video feeds and recognition models.
+* **Security:** The practice of protecting machine learning systems and their data from unauthorized access, hacking, theft, and misuse. A secure system safeguards its data and operations to ensure integrity and confidentiality. For example, in the context of the home security camera, security measures prevent hackers from intercepting live video feeds or tampering with stored footage and ensure the model itself remains uncompromised.
 
-* **Threat:** Using our home security camera example, a threat could be a hacker trying to access live feeds or stored videos or using false inputs to trick the system.
+* **Threat:** Refers to any potential danger, malicious actor, or harmful event that aims to exploit weaknesses in a system to compromise its security or privacy. A threat is the external force or intent that seeks to cause harm. Using the home security camera example, a threat could involve a hacker attempting to access live streams, steal stored videos, or deceive the system with false inputs to bypass facial recognition.
 
-* **Vulnerability:** A common vulnerability might be a poorly secured network through which the camera connects to the internet, which could be exploited to access the data.
+* **Vulnerability:** Refers to a weakness, flaw, or gap in the system that creates the opportunity for a threat to succeed. Vulnerabilities are the points of exposure that threats target. Vulnerabilities can exist in hardware, software, or network configurations. For instance, if the home security camera connects to the internet through an unsecured Wi-Fi network, this vulnerability could allow attackers to intercept or manipulate the video data.
 
 ## Historical Precedents