Category: Computer Science homework help

 
Purpose
In this assignment, you will analyze recent legislation related to privacy and evaluate the impact of that legislation on an organization.
Assignment Instructions
Assume you are an IT security specialist for a large U.S. online retail organization that does business internationally. Your CIO has asked you to thoroughly review the General Data Protection Regulation (GDPR) in the European Union. He wants to understand exactly what the organization must do to comply with this regulation when doing business with EU customers.
Provide a detailed discussion about the rules for businesses and the rights of EU citizens.
Include a discussion of the following:
What does the GDPR govern?
What rights do EU citizens have with regard to their data?
What is considered personal data under this regulation?
What is considered data processing under this regulation?
Describe the role of the data protection authorities (DPAs).
Discuss, in detail, how the GDPR will change business and security operations for your organization. Provide the CIO with a recommended checklist for GDPR compliance, and discuss processes and policies that may need to be changed in order to comply with GDPR.
In your conclusion, address what you think will be the financial impact to the organization, both in terms of compliance and any lack of compliance. 
Assignment Requirements
The paper should be 3–4 pages.
Use Times New Roman 12 pt font.
Use APA formatting for paper, citations, and references.
Be sure to cite your sources and provide the appropriate references.

 
Purpose
Regulations, standards, and frameworks are complex. Doing a deep dive into one of those standards, Zero Trust will allow you to learn how to read a standard thoroughly and what elements of the standard are essential, as well as how to locate those elements within the written standard. You will also evaluate the effectiveness of a standard, providing supporting examples.
Assignment Instructions
Use the materials from your reading, particularly the material specific to CISA’s Zero Trust Model and NIST 80-207 Zero Trust Architecture. In addition, research the Internet to provide the required responses.
Provide an in-depth explanation of the following about the Zero Trust model and framework:
Explain the events that led to the development of the Zero Trust Model.
Explain the goals that the model seeks to achieve.
Provide an overview of the IT and Cybersecurity departments’ role in achieving Zero Trust.
Explain how audits and assessments help achieve or measure compliance.
What is required to comply with NIST 800-207?
What challenges exist when moving to the Zero Trust Model?
Assess the value of the Zero Trust Model as organizations move to cloud-based assets, remote workers, and Bring Your Own Device (BYOD) environments.
Conclusion
Assignment Requirements
4–5 pages of content (exclusive of cover sheet and references page), using Times New Roman font style, 12 point, double-spaced, using correct APA formatting, and include a cover sheet, table of contents, abstract, and reference page(s)
At least 1 credible source cited and referenced
No more than 1 table or figure
No spelling errors
No grammar errors
No APA errors

 
Perhaps one of the business areas that faces the greatest risk each day is the lending industry. Banks, mortgage companies, and other types of lenders face one specific risk many times every day: Are they going to be paid back when they make a loan? Organizations that make their money by lending money must be able to anticipate risk and predict the likelihood that they will be paid back, with interest, or else their business model will fail and they will have to close their doors. In this Assignment, you will use R with two data sets to predict the risk of loan default for a lender, and then report and explain your results. 
Assignment Instructions 
Complete the following steps: 
Using the university’s online Library and Internet resources, research the lending industry. In a Word document, prepare a risk management plan outline for loan default risk faced by lenders. Include all five parts of risk management planning: Identification, Understanding, Data Preparation, Modeling and Application. Cite all sources used to prepare your risk management plan. 
Download the Loans.csv and Applicants.csv files. Import both of these as data frames into RStudio. Give each a descriptive name. Show this in your Word document.
Using the Loans.csv file, build a logistic regression model to predict the “Good Risk” dependent variable (use family=binomial() in the glm function in R). In this column, ‘1’ indicates that making the loan is a good risk for the lender; ‘0’ indicates that making the loan is a bad risk. Make sure that you do not use the Applicant ID as an independent variable! You will need to load the MASS package in R by issuing library(MASS), before using the glm function to build your model. Show the creation of the model in your Word document. 
In your Word document, document your logistic model’s output, and specifically explain which independent variables have the most predictive power and which have the least. Make sure you identify how you know, and explain why it matters. 
Apply your logistic regression model to the data in Applicants.csv to generate predictions of “Good Risk” for each loan applicant. If your glm model is stored in an R object called ‘LoanModel’, for example, and your Applicants.csv data is in a frame called ‘Appl’, then you would issue a command that looks like this: LoanPredictions <- predict(LoanModel, Appl, type=“response”). Document the application of your model to the Applications data in your Word document.  In your Word document, interpret your predictions for the Applicants.csv data. Specifically address the following:  How many loans do you predict to be a good risk for the lender? How many are predicted to be a bad risk? What are your highest and lowest post-probability percentages for predictions? How many loans have at least a 75% post-probability percentage and what does that mean for the lender? How many loans have less than a 25% post-probability percentage and what does that mean for the lender? Suppose that the lender is willing to accept a little higher risk and has decided they will make loans to applicants who have post-probability percentages between 40% and 65%. List two things the lender could do to mitigate risk when lending to this group, and explain how these will help.  Make sure that you cite at least five supporting sources beyond the textbook in support of your writing and explanations. Cite correctly in APA format. Assignment Requirements  Prepare your Assignment submission in Microsoft Word following standard APA formatting guidelines: Double spaced, Times New Roman 12-point font, one inch margins on all sides. Include a title page, table of contents and references page. You do not need to write an abstract. Label all tables and figures. Cite sources appropriately both in the text of your writing (parenthetical citations) and on your references page (full APA citation format).  For more information on APA style formatting, refer to the resources in the Academic Tools section of this course. 

 
Generative adversarial nets are mentioned in 2014 by Ian Goodfellow et al. 
Why is generative adversarial network a key turning point in the history of generative modeling?
Why is the field of image generation important? 

A privacy impact assessment (PIA) is a process to help you identify and minimize data privacy risk. Specifically, this type of assessment helps identify the risks to an individual when an organization collects personal information for a business purpose. There are many reasons an organization might collect personal data. For example, all businesses must collect personal information from employees to process payroll taxes. Many businesses collect personal information from customers to ship goods and services or conduct research to create new products.An organization should complete a PIA any time it intends to collect a new data element from an individual, such as name, date of birth, age, race, sex, address, biometric identifier, or any other element of personal data. Completing a PIA helps an organization think deeply about privacy issues and risks related to collecting specific types of data. To complete a PIA, an organization should:
Clearly specify the data that it wishes to collect from a person.
Clearly document why it must collect that data.
Describe how the data will be collected, used, and stored.
Document the risks of collecting, using, and storing, the data.
Describe the measures that the organization will take to reduce the risks of collecting, using, and storing the data.
Organizational leaders will use the information provided in a PIA to determine whether the need for collecting the data outweighs the risks to the organization that are posed by collecting it. This is a business decision. Stakeholders such as legal counsel, human resources professionals, and information security and privacy professionals will often help prepare and review the PIA. An organization usually does not need to share its PIA with other entities.In this lab, you will learn about and prepare a privacy impact assessment for a fictitious organization.Lab OverviewThis lab has three parts, which should be completed in the order specified.
In the first part of the lab, you will document the personal information that a company seeks to collect.
In the second part of the lab, you will document the risks of data collection.
In the third part of the lab, you will explain why (or why not) the company should collect the personal information specified in Part 1.
Finally, if assigned by your instructor, you will complete a challenge exercise that allows you to use the skills you learned in the lab to conduct independent, unguided work – similar to what you will encounter in a real-world situation.Learning ObjectivesUpon completing this lab, you will be able to:
Identify personal data elements.
Describe risks to the collection of personal data.
Justify data collection activities.

 
Your job is to:
1) implement the one algorithm in these files that is not already implemented (merge sort),
2) Tell me in a comment in your code three things:
what the runtime of this algorithm is
whether it is “destructive”
whether it is “in-place”
3) submit timing data along with your code to show that your code actually has the runtime you claim it does.
Your submission will be:
– A zipped copy of all the files I’m providing, with the unimplemented algorithm implemented and the comments attached to taht algorithm indicating its properties (see above).
– And, in your zip file, you should include some kind of graph showing the growth of the runtime of your implementation of the algorithm, as determined by running it under different conditions and timing it, along with the raw timing data you used to make the graph.You can make the graph however you like (hand-drawn is fine).