Merge pull request #453 from Sara-Khosravi/edit2-privacy-security

Edit2 privacy security

contents/benchmarking/benchmarking.qmd

@@ -242,6 +242,7 @@ Benchmarking the training phase provides insights into how different model archi
 
 On the other hand, benchmarking inference evaluates model performance in real-world conditions after deployment. Key metrics include latency, throughput, memory footprint, and power consumption. This type of benchmarking determines if a model meets the requirements of its target application regarding response time and device constraints. However, we will discuss these broadly to ensure a general understanding.
 
+
 ### Training Benchmarks
 
 Training represents the phase where the system processes and ingests raw data to adjust and refine its parameters. Therefore, it is an algorithmic activity and involves system-level considerations, including data pipelines, storage, computing resources, and orchestration mechanisms. The goal is to ensure that the ML system can efficiently learn from data, optimizing both the model's performance and the system's resource utilization.

contents/introduction/introduction.qmd

@@ -8,7 +8,7 @@ bibliography: introduction.bib
 
 ## Overview
 
-In the early 1990s, [Mark Weiser](https://en.wikipedia.org/wiki/Mark_Weiser), a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed "ubiquitous computing," promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser's vision, thanks to the advent and proliferation of machine learning systems.
+In the early 1990s, [Mark Weiser](https://en.wikipedia.org/wiki/Mark_Weiser), a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. This was succintly captured in the paper he wrote on "The Computer for the 21st Century" (@fig-ubiqutous). He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed "ubiquitous computing," promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser's vision, thanks to the advent and proliferation of machine learning systems.
 
 ![Ubiqutous computing.](images/png/21st_computer.png){#fig-ubiqutous width=50%}
 

contents/privacy_security/privacy_security.qmd

@@ -416,23 +416,23 @@ While the above are not directly connected with ML, consider the example of a sm
 
 Such leaks are a privacy issue and a potential entry point for more damaging exploits. The exposure of training data, model parameters, or ML outputs from a leak could help adversaries construct adversarial examples or reverse-engineer models. Access through a leaky interface could also be used to alter an embedded device's firmware, loading it with malicious code that could turn off the device, intercept data, or use it in botnet attacks.
 
-To mitigate these risks, a multi-layered approach is necessary, spanning technical controls like authentication, encryption, anomaly detection, policies and processes like interface inventories, access controls, auditing, and secure development practices. Turning off unnecessary interfaces and compartmentalizing risks via a zero-trust model provide additional protection.
+A multi-layered approach is necessary to mitigate these risks, spanning technical controls like authentication, encryption, anomaly detection, policies and processes like interface inventories, access controls, auditing, and secure development practices. Turning off unnecessary interfaces and compartmentalizing risks via a zero-trust model provide additional protection.
 
 As designers of embedded ML systems, we should assess interfaces early in development and continually monitor them post-deployment as part of an end-to-end security lifecycle. Understanding and securing interfaces is crucial for ensuring the overall security of embedded ML.
 
 ### Counterfeit Hardware
 
 ML systems are only as reliable as the underlying hardware. In an era where hardware components are global commodities, the rise of counterfeit or cloned hardware presents a significant challenge. Counterfeit hardware encompasses any components that are unauthorized reproductions of original parts. Counterfeit components infiltrate ML systems through complex supply chains that stretch across borders and involve numerous stages from manufacture to delivery.
 
-A single lapse in the supply chain's integrity can result in the insertion of counterfeit parts designed to closely imitate the functions and appearance of genuine hardware. For instance, a facial recognition system for high-security access control may be compromised if equipped with counterfeit processors. These processors could fail to accurately process and verify biometric data, potentially allowing unauthorized individuals to access restricted areas.
+A single lapse in the supply chain's integrity can result in the insertion of counterfeit parts designed to imitate the functions and appearance of genuine hardware closely. For instance, a facial recognition system for high-security access control may be compromised if equipped with counterfeit processors. These processors could fail to accurately process and verify biometric data, potentially allowing unauthorized individuals to access restricted areas.
 
 The challenge with counterfeit hardware is multifaceted. It undermines the quality and reliability of ML systems, as these components may degrade faster or perform unpredictably due to substandard manufacturing. The security risks are also profound; counterfeit hardware can contain vulnerabilities ripe for exploitation by malicious actors. For example, a cloned network router in an ML data center might include a hidden backdoor, enabling data interception or network intrusion without detection.
 
 Furthermore, counterfeit hardware poses legal and compliance risks. Companies inadvertently utilizing counterfeit parts in their ML systems may face serious legal repercussions, including fines and sanctions for failing to comply with industry regulations and standards. This is particularly true for sectors where compliance with specific safety and privacy regulations is mandatory, such as healthcare and finance.
 
-The issue of counterfeit hardware is exacerbated by economic pressures to reduce costs, which can compel businesses to source from lower-cost suppliers without stringent verification processes. This economizing can inadvertently introduce counterfeit parts into otherwise secure systems. Additionally, detecting these counterfeits is inherently difficult since they are created to pass as the original components, often requiring sophisticated equipment and expertise to identify.
+Economic pressures to reduce costs exacerbate the issue of counterfeit hardware and compel businesses to source from lower-cost suppliers without stringent verification processes. This economizing can inadvertently introduce counterfeit parts into otherwise secure systems. Additionally, detecting these counterfeits is inherently tricky since they are created to pass as the original components, often requiring sophisticated equipment and expertise to identify.
 
-In ML, where decisions are made in real time and based on complex computations, the consequences of hardware failure are inconvenient and potentially dangerous. Stakeholders in the field of ML need to understand these risks thoroughly. The issues presented by counterfeit hardware necessitate a deep dive into the current challenges facing ML system integrity and emphasize the importance of vigilant, informed management of the hardware life cycle within these advanced systems.
+In the field of ML, where real-time decisions and complex computations are the norm, the implications of hardware failure can be inconvenient and potentially dangerous. It is crucial for stakeholders to be fully aware of these risks. The challenges posed by counterfeit hardware call for a comprehensive understanding of the current threats to ML system integrity. This underscores the need for proactive, informed management of the hardware life cycle within these advanced systems.
 
 ### Supply Chain Risks
 
@@ -454,7 +454,7 @@ In 2018, Bloomberg Businessweek published an alarming [story](https://www.bloomb
 
 If true, this would allow hackers to spy on private data or even tamper with systems. However, after investigating, Apple and Amazon found no proof that such hacked Supermicro hardware existed. Other experts questioned whether the Bloomberg article was accurate reporting.
 
-Whether the story is completely true or not is not our concern from a pedagogical viewpoint. However, this incident drew attention to the risks of global supply chains for hardware, especially manufactured in China. When companies outsource and buy hardware components from vendors worldwide, there needs to be more visibility into the process. In this complex global pipeline, there are concerns that counterfeits or tampered hardware could be slipped in somewhere along the way without tech companies realizing it. Companies relying too much on single manufacturers or distributors creates risk. For instance, due to the over-reliance on [TSMC](https://www.tsmc.com/english) for semiconductor manufacturing, the U.S. has invested 50 billion dollars into the [CHIPS Act](https://www.whitehouse.gov/briefing-room/statements-releases/2022/08/09/fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chains-and-counter-china/).
+Whether the story is entirely accurate or not is not our concern from a pedagogical viewpoint. However, this incident drew attention to the risks of global supply chains for hardware primarily manufactured in China. When companies outsource and buy hardware components from vendors worldwide, there needs to be more visibility into the process. In this complex global pipeline, there are concerns that counterfeits or tampered hardware could be slipped in somewhere along the way without tech companies realizing it. Companies relying too much on single manufacturers or distributors creates risk. For instance, due to the over-reliance on [TSMC](https://www.tsmc.com/english) for semiconductor manufacturing, the U.S. has invested 50 billion dollars into the [CHIPS Act](https://www.whitehouse.gov/briefing-room/statements-releases/2022/08/09/fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chains-and-counter-china/).
 
 As ML moves into more critical systems, verifying hardware integrity from design through production and delivery is crucial. The reported Supermicro backdoor demonstrated that for ML security, we cannot take global supply chains and manufacturing for granted. We must inspect and validate hardware at every link in the chain.