Big Data Systems (CS 265) – 2023 Spring

Module Topic: Privacy and Promoting Public Health with Big Data Systems
Module Author: Michael Pope

Course Level: Graduate
AY: 2022-2023

Course Description: “Big data is everywhere. A fundamental goal across numerous modern businesses and sciences is to be able to utilize as many machines as possible, to consume as much information as possible and as fast as possible. The big challenge is how to turn data into useful knowledge. This is a moving target as both the underlying hardware and our ability to collect data evolve. In this class, we discuss how to design data systems, data structures, and algorithms for key data-driven areas, including relational systems, distributed systems, graph systems, noSQL, newSQL, machine learning, and neural networks. We see how they all rely on the same set of very basic concepts and we learn how to synthesize efficient solutions for any problem across these areas using those basic concepts.”Big data is everywhere. A fundamental goal across numerous modern businesses and sciences is to be able to utilize as many machines as possible, to consume as much information as possible and as fast as possible. The big challenge is how to turn data into useful knowledge. This is a moving target as both the underlying hardware and our ability to collect data evolve. In this class, we discuss how to design data systems, data structures, and algorithms for key data-driven areas, including relational systems, distributed systems, graph systems, noSQL, newSQL, machine learning, and neural networks. We see how they all rely on the same set of very basic concepts and we learn how to synthesize efficient solutions for any problem across these areas using those basic concepts.” (Harvard course catalog. Course website)

Semesters Taught: Spring 2019, Spring 2020, Spring 2023

    Tags

Module Overview

This module focuses on efforts to preserve privacy in contexts where big data systems can promote public goods. The primary case study, involving contact tracing applications, raises questions about leveraging available data in the service of public health and preserving privacy. In particular, the module examines how centralized and decentralized data collection and storage approaches can promote autonomy, democratic ideals, and protections from harm. Finally, the module concludes with a discussion of the limits of privacy.

    Connection to Course Technical Material

This course focuses on cutting-edge research into the design and deployment of big data systems. The module integrates considerations of privacy’s social and ethical importance into the course’s focus on leveraging big data to enhance system performance.

Goals

Module Goals

1. Identify potential privacy violations in leveraging big data systems for contact tracing.
2. Discuss ways that data structure can preserve privacy or fail to preserve privacy.
3. Examine how performance goals interact with valuable functions of privacy.

    Key Philosophical Questions

1. What is privacy?
2. Why should data system design choices protect privacy?
3. What are the limits of privacy restrictions?

Materials

    Key Philosophical Concepts

• Privacy
• Public good
• Value trade-offs
• Trust and trustworthiness

    Assigned Readings

• Warzel C. and Ngu A. (2019). “Google’s 4,000-word privacy policy is a secret history of the internet.” The New York Times.
• Sharon T. (2021). “Blind-sided by privacy? Digital contact tracing, the Apple/Google API and big tech’s newfound role as global health policy makers.” Ethics and Information Technology.

Implementation

Class Agenda

1. Introduction to contact tracing techniques, including its history
2. Discussion: leveraging big data systems for contact tracing
3. Case study: Apple and Google’s API for exposure notification
4. Discussion: balancing privacy concerns and performance maximization
5. Final activity and debrief: student identification and discussion of privacy concerns through various case studies

    Sample Class Activity

Before raising privacy concerns, students break into groups and discuss the types of data most relevant to contact tracing, including efficiency considerations for gathering and storing the relevant data. Through group discussion, students write data sources on post-it notes and place them on the board. After collecting all input, the class generates possible reasons for utilizing or avoiding data sources in contact tracing, facilitating a conceptualization of privacy through discussion. Potentially problematic data sources are deliberatively ‘taken off the table’ by moving them to a special area of the board.

    Module Assignment

There was no assignment for this module.

Lessons Learned

Student engagement and feedback for this module was universally positive. An important upshot of student feedback is that modules are more impactful to the extent that they integrate students’ experiences. Inviting students to think about how privacy functions in their own lives promotes their participation in identifying privacy’s value. For example, student reflection (including private reflection) about activities they would not engage in if they had no privacy, what powers they are subjected to due to our current data practices, etc. increases buy-in and naturally enhances discussions.