Privacy

Control Hijacking Attacks: defenses

Sandboxing

Web Defenses

Spectre Attack

Control Hijacking Attacks: exploits

Isolation

Web Attacks

Intel SGX

Network Security

Network Defenses

Access control

Web Security

HTTPS

Mobile Platform Security Architecture

Internet Security

Censorship

System Security

Operating System Security

Web Security Model

Processor and Microarchitecture Security

Android Security

DoS Attacks

Principle of least privilege

Testing for vulnerabilities

Cryptography

Mobile Security

Internet Protocols

Anonymity

CS 155 Computer and Network Security Stanford University Spring 2022 This course focuses on principles of computer systems and network security, exploring different attack techniques and corresponding defenses. Course projects aim at building reliable code and understanding attacks. Prior knowledge in operating systems, networking protocols, and basic programming languages is needed. The course covers principles of computer systems and network security. We will discuss various attack techniques and how to defend against them. Topics include network attacks and defenses, operating system holes, web security, e-mail, botnet, malware, social engineering attacks, privacy, and digital rights management. Course projects will focus on building reliable code and understanding attacks. The course is intended for junior and senior undergraduates and first year graduate students.  The course requires an understanding of operating systems, networking protocols, and a basic understanding of programming languages. Programming projects will be done primarily in C and JavaScript, but other languages may be needed.

Pre-requisites: CS110/CS111 (Principles of Computer Systems) There is no textbook for the class.

Instead, we will use research papers posted on the course [syllabus page](https://cs155.stanford.edu/syllabus.html) for every lecture.

CS 155 Computer and Network Security

Computer Security and Cryptography

Histograms

Pivots

Models

P-Value

Confidence Intervals

Sample Means

Least Squares

Bayes' theorem

Data type

Iteration 

Census

Function (computer programming)

Joins

Chance

Comparing Distributions

A/B Testing

Center and Spread

Correlation

Residuals

Classification

Conditional probability

Tables

Charts

Groups

Conditional (computer programming)

Sampling

Decisions

Causality

Normal distribution

Linear regression

Regression Inference

Classifiers

Data 8: The Foundations of Data Science UC Berkeley Fall 2022 UC Berkeley's course blends inferential thinking, computational thinking, and real-world relevance, offering students hands-on analysis of real-world datasets. It covers critical concepts in computer programming, statistical inference, privacy, and study design. Foundations of Data Science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It also delves into social issues surrounding data analysis such as privacy and study design.  This course does not have any prerequisites beyond high-school algebra. The curriculum and format is designed specifically for students who have not previously taken statistics or computer science courses. Students with some prior experience in either statistics or computing are welcome to enroll and will find much of interest due to the innovative nature of the course. Students who have taken several statistics or computer science courses should instead take a more advanced course. Our primary text is an online book called [Computational and Inferential Thinking: The Foundations of Data Science](http://inferentialthinking.com/). This text was written for the course by the course instructors. A complete PDF of the textbook can be found in the Student Materials Google Drive.

The computing platform for the course is hosted at [data8.datahub.berkeley.edu](file:///Users/jweichert/Desktop/Data%208/data-8.github.io/fa22/data8.datahub.berkeley.edu). Students find it convenient to use their own computer for the course. If you do not have adequate access to a personal computer, we can help you borrow a machine; please contact data8@berkeley.edu.

Data 8: The Foundations of Data Science

Data Science

Free Speech

Social Connection and Time Well-Spent

Government Censorship and Surveilence

Memes and Viral Content

Framing Society and Identity

Software Risks

The Optimization Mindset, Tech Policy, Big Tech

Education

Labor and automation

Algorithmic bias

Algorithmic fairness

CS 195 Social Implications of Computer Technology UC Berkeley Fall 2022 This course is an in-depth discussion on the societal impacts of computer technology. The course content is drawn from various fields including sociology, philosophy, economics, and public policy. Students are expected to explore topics like privacy, algorithmic bias, tech policy, and the implications of tech on education and jobs. CS 195 is a discussion-intensive course about the social implications of computer technology. The purpose of this course is to help students make informed and thoughtful choices about their careers, participation in society, and future development activities. Readings and lecture topics are drawn from a range of fields that together seek to describe our contemporary global society: sociology, philosophy, economics, public policy, etc.   

CS 195 Social Implications of Computer Technology

Computer Ethics

Unix

Bitwise Operations

x86-64

Assembly

Stack (abstract data type)

C (programming language)

C Strings

Heap (data structure)

Assembly: Arithmetic and Logic

Trust

Integers

C Generics

Assembly: Control Flow

Managing the Heap

Buffer overflow

Data Representation

Array (data structure)

Function Pointers

Assembly: Function Call

Optimization

Overflow

Pointer (computer programming)

Reverse Engineering

Caching

CS 107 Computer Organization & Systems Stanford University Autumn 2022 This Stanford University course delves into the depths of computer systems and programming. It continues from the introductory sequence, expanding students' programming experience using the C language, exploring machine-level code, computer arithmetic, memory management, and more. CS107 is the third course in Stanford's introductory programming sequence. The CS106 courses provide you with a solid foundation in programming methodology and abstractions, and CS107 follows on this to build up and expand your breadth and depth of programming experience and techniques. The course will work from the C programming language down to the microprocessor to de-mystify the machine. With a complete understanding of how computer systems execute programs and manipulate data, you will become a more effective programmer, especially in dealing with issues of debugging, performance, memory, and robustness. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, optimization of memory and runtime performance, and memory organization and management.

### CS107A

CS107A, also called Pathfinders (or ACE), is a supplementary instruction program that meets for a weekly section and holds Pathfinders-specific review sessions. CS107A is application-only; please see the [FAQ](https://web.stanford.edu/class/archive/cs/cs107/cs107.1232/faq.html) page for more information. The goals for CS107 are for students to gain mastery of

- writing C programs with complex use of memory and pointers
- an accurate model of the address space and compile/runtime behavior of C programs

to achieve **competence** in

- translating C to/from assembly
- writing programs that respect the limitations of computer arithmetic
- identifying bottlenecks and improving runtime performance
- working effectively in a Unix development environment
- using ethical frameworks and case studies to inform decision-making

and have **exposure** to

a working understanding of the basics of computer architecture The prerequisite for CS107 is CS106B/X (or equivalent). You should have practical C/C++ skills using recursion, dynamic data structures (pointers, linked lists, trees, graphs), data abstraction, classic data structures (lists, stacks, queues, sets, maps), and standard algorithms (searching, sorting, hashing). You should have an appreciation of the intrinsic value of good engineering and design and you will be expected to produce well-decomposed, readable code. Come talk with us if you need help determining the right placement for you. **Required**: Bryant & O’Hallaron. Computer Systems: A Programmer’s Perspective. 3rd Edition.

The bookstore has a less-expensive custom version of this textbook for our course that includes only the chapters we will cover; you can also use the regular full 3rd edition. You will need the 3rd edition of the textbook, which has substantial updates from IA32 to x86-64. There will be assigned readings from this textbook that are important preparation for lecture and lab. For readings that we assign, you can view a free digital PDF copy of the textbook on Canvas under the "Files" tab.

**Strongly recommended**: We also strongly recommend you have a "C language goto" in whatever form works best for you: textbook, tutorial, reference sheet, website, etc. As one suggestion, The C Programming Language by Kernighan and Ritchie is the classic text and a digital copy is available for checkout via [Open Library](https://openlibrary.org/books/OL2030445M/The_C_Programming_Language) (make a free account to "borrow" it digitally). Another option is Nick Parlante's Essential C reader PDF [available here](http://cslibrary.stanford.edu/101).

There is also a CS107 reader written by Chris Gregg, another CS107 instructor: [https://web.stanford.edu/~cgregg/cgi-bin/107-reader](https://web.stanford.edu/~cgregg/cgi-bin/107-reader), which covers all of the course topics in detail.

Our lecture readings pull from Bryant & O'Halloran, Kernighan & Ritchie, and Essential C.

CS 107 Computer Organization & Systems

Computer Systems

Ethics

Algorithmic decision-making

Accountability

Surveillance

Civil liberties

Artificial intelligence

Platforms

Social media

Misinformation

Blockchain

Cryptocurrency

Decentralization

CS 182: Ethics, Public Policy, and Technological Change Stanford University Winter 2023 This course examines the intersections of philosophy, public policy, social science, and engineering in the context of recent computing technology and platforms. Key areas of focus include algorithmic decision-making, data privacy, AI, the influence of private computing platforms, and issues of diversity in tech. Students need to have completed CS106A. Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments.  ### Menu of Topics

The course will focus on six frontiers that (a) you are likely to play a role in shaping over the next decade and (b) where engagement with material from philosophy, social science, and public policy is likely to be helpful.

1. Algorithmic Decision-Making 
2. Political Economy of Tech Sector
3. Data Collection, Privacy, and Civil Liberties 
4. Artificial Intelligence and Autonomous Systems
5. Power of Private Platforms
6. Blockchain, Decentralization, and Cryptocurrency
 CS106A ### Cross-Cutting Themes

Throughout the course, we will foreground two cross-cutting themes:

- First, issues of diversity, equity, and inclusion which focus our attention on who has power, voice, and influence in shaping the design and regulation of technology.
- Second, issues of political economy which focus our attention on the interplay of markets and governments in shaping what technology gets built, who benefits from that technology, and how any harmful impacts are/are not mitigated.