Data, privacy, and the greater good

By Eric Horvitz, Deirdre K. Mulligan. 2015

257 citations recorded by the source corpus.

Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhancing health and wellbeing. However, they raise questions about how to best address potential threats to privacy while reaping benefits for individuals and to society as a whole. The use of machine learning to make leaps across informational and social contexts to infer health conditions and risks from nonmedical data provides representative scenarios for reflections on directions with balancing innovation and regulation.

Subfields: Epidemiology, General Health Professions, Organizational Behavior and Human Resource Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. Emre Kıcıman
2. Glen A. Coppersmith
3. Alexandra Olteanu
4. Carlos Castillo
5. Michael J. Paul
6. Fernando Diaz
7. Mark Dredze

In General Health Professions

1. Michael J. Paul
2. Fernando Diaz
3. Carlos Castillo
4. Emre Kıcıman
5. Alexandra Olteanu

In Organizational Behavior and Human Resource Management

1. Michael J. Paul
2. Emre Kıcıman

Citations in the corpus

• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Providing users information about how their data are used is a key element concerning their autonomy (Horvitz and Mulligan, 2015).”
• Deep Learning for Health Informatics (2017, background): “Therefore, it has become evident that we need to strike a balance between ensuring individuals can control access to their private medical information and providing pathways on how to make information available for public health studies [117].”
• Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, background): “Such difficulties and restrictions on medical data sharing are the major hindering factor in the development of advanced ML techniques for healthcare [9].”
• Interpretable Machine Learning : Lessons from Topic Modeling (2016, background): “As pointed out by [10], this is a challenging task when using complex models such as deep neural networks.”
• Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (2021, background): “Horvitz and Mulligan (2015) provide one short, useful discussion specifically focused on data and privacy, and Benton et al. (2017) and Chancellor et al. (2019) discuss ethical issues specifically with regard to social media and work on mental health.”
• Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020, background): “t also the industry competition, the complicated administrative procedures, and essentially, the data protection regulations and restrictions such as the EU General Data Protection Regulation (GDPR)2 [1]. In traditional ML algorithms, large-scale data collection and processing at a powerful cloud-ba”