MBDA 730 Week 3 Homework

Final Project Introduction

The final project for MBDA 730 requires students to explore an ongoing series of projects published by the New York Times Privacy Project.

Part 1.

The project is required to be a RMD file that compares and critiques the ethics and fairness framework among three projects. Within the submission students are required to provide a summary of each project, identify unwanted or unexpected consequences of each project. Identify the impacts from each of the consequences. Reference the source of harm from the consequences. Finally, based on the recommendation systems in AI provide recommendations for implementation into each project.

Part 2.

Part 2 of the final project asks students to reference current efforts to address issues presented in Part 1 of the assignment. Current projects can be found by exploring the Data Privacy Lab.

Based on the projects selected from Part 1 (facial recognition, policy gaps, and genomic privacy) pick three reserach projects that are attempting to address those data privacy concerns. This section of the paper should describe how to New York Times Project and the Privacy Project are related and explain how methods presented in the Privacy Project will help solve Privacy issues presented in the New York Time Project.

Homework Assignment 2

I would like to explore facial recognition, data privacy legislation, and genomic privacy during my final project. The three topics, like most, are intertwined with each other. I will address these issues through a tiered approach of importance. First, I believe legislation and regulations will enable government and private privacy protection measures as seen through how hospitals protect patient information, how government and businesses use facial recognition, and creating genomic privacy. Only though legislation will it be reasonable to assume there will be meaningful protections for how data is shared, de-identified, and collected in the governmental and private domains.

Privacy Protection Laws and Regulations

To adress the issue of lagging privacy protection policy in the United States, I have found the project The Government Protects our Food and Cars. Why Not our Data? (Singer, 2019). Within the New York Times article there is no shortage of data sources to justify the argument from the author that the United States requires more robust data privacy legislation. The author references famous data security breaches like what occured with Strava location data, Apple facetime security, and Fitbit vulnerabilities. However, the bulk of the authors argument circulates around the severe inquiry between what the government regulates vs. how it regulates data. The culmination of the authors argument is that a seperate independent agency similar to the Food and Drug Administration (FDA) should be created to address how the government oversees how data is protected within the United States.

The data privacy lab project that adderesses the need for a HIPPA privacy protection legistlation is Patient Privacy Risks in U.S. Supreme Court Case Sorrell vs. IMS Health (Sweeney, n.d.). Just like the New York Times Privacy Project article, the court case found that there was a lack in regulations in how HIPPA data is protected to prevent re-identification and how re-identification could negatively impact society.

Facial Recognition

To address data privacy issues regarding facial recognition software, I will explore the project You’re In a Police Line Up Right Now (Garvie, 2019). The article presents vignettes that show common pitfalls from the misuse, bias, and protection of facial recognition in application. While the video presents other data ethics concerns such as informed consent of how our data is shared, and general anonymity concerns, the focus is how law enforcement leverages facial recognition technology taken from photos collected from when Americans apply for a drivers license.

The privacy lab project that outlines recommendations for implementation of privacy protection with facial regocnition technology is Preserving Privacy by De-Identifying Facial Images (Newton, 2003). In the project, algorithms are proposed that continue to allow the benefit of facial recognition technology while continuing to prevent potential harms that can occur from its use. The author’s present several methods to de identify facial images without removing the value of facial regocntion technoliogy by understanding that methods such as a completely pixelated face would render a systems useless.

Genomic Privacy

To address genomic privacy I will explore the project Why Are You Publicly Sharing Your Child’s DNA Information (Bala, 2019). The focus of this section will address how genomic privacy is required for anonymity and illustrate the pitfalls from release of genomic information. In the New Yor Times Privacy Project article the author presents dangers from employing services from companies like 23 and Me, Map my Gene, and Org3n. The author explains how by utilizing the services, consumers inadvertently share familal information and erode the privacy of the consumer’s family.

The privacy lab project that presents solutions for the issue is Why Pseudonyms Don’t Anonymize: A Computational Re-identification Analysis of Genomic Data Privacy Protection Systems (Malin, 2003). While the New York Times Project presents dangers in how family members can be identified through genomic testing, the Privacy Project article aims to present methods to protect genomic data from re-identification attacks. The methods presented in this paper strenthen how genomic data is protected without removing the value from analysis of genome data.

References

Bala, N. (2020). Why Are You Sharing Your Child’s DNA Information? New York Times. Retieved January 28, 2023 from https://www.nytimes.com/2020/01/02/opinion/dna-test-privacy-children.html

Garvie, C. (2019). You’re In A Police Lineup Right Now. New York Times. Retrieved January 27, 2023 from https://www.nytimes.com/2019/10/15/opinion/facial-recognition-police.html

Malin, B. (2003). Why Pseudonyms Don’t Anonymize: A Computational Re-identification Analysis of Genomic Data Privacy Protection Systems. Carnegie Melon University. Retrieved January 28, 2023 from https://dataprivacylab.org/dataprivacy/projects/linkage/lidap-wp19.pdf

Newton, E., Sweeney, L., Malin, B. (2003). Preserving Privacy by De-Identifying Facial Images. Carnegie Melon University. Retrieved January 27, 2023 from https://dataprivacylab.org/dataprivacy/projects/video/paper.pdf

Singer, N. (2019). The Government Protects out Food and Cars. Why Not Our Data? New York Times. Retrieved January 26, 2023 from https://www.nytimes.com/2019/11/02/sunday-review/data-protection-privacy.html

Sweeney, L. (n.d.). Patient Privacy Risks in U.S. Supreme Court Case Sorrell vs. IMS Health. Privacy Lab Project. Retrieved January 26, from https://dataprivacylab.org/projects/identifiability/pharma2.pdf