Marika Swanberg

🇸🇪 → 🇺🇸

I am half Swedish and half American, bilingual, and grew up in both countries.

About Me

I am a machine learning engineer (and researcher) on Google's Privacy Sandbox team in NYC. I graduated in 2024 with my PhD from Boston University, advised by Adam Smith. During my PhD I interned at Tumult Labs and twice at Google Research, in addition to being a visiting assistant professor at Reed College.

I enjoy thinking about privacy risks against attackers with varying capabilities. I am interested in designing accurate and scalable differentially private (DP) algorithms for real-world deployments. Previously, I've dabbled in cryptography, theory of DP, and their connections to legal questions.

I've served on the program committees for Theory and Practice of Differential Privacy (2023, 2024), and Conference on Computer and Communications Security (2024), and was a reviewer for: Journal of Privacy and Confidentiality (2024) and Theory of Cryptography (2020).

In my free time, I enjoy entertaining my border collie.

Marika's Google Scholar

I'm interested in operationalizing the theory of privacy in real-world systems.

Publications and manuscripts

Authors listed in alphabetical order by last name unless indicated by an asterisk.

Privacy in Metalearning and Multitask Learning: Modeling and Separations. Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan Ullman. AISTATS 2025.
*Measuring Memorization through Probabilistic Discoverable Extraction. Jamie Hayes, Marika Swanberg, Harsh Chaudhari, Itay Yona, Ilia Shumailov.
Attaxonomy: Unpacking Differential Privacy Guarantees Against Practical Adversaries. Rachel Cummings, Shlomi Hod, Jayshree Sarathy, Marika Swanberg. FORC (non-archival) 2024, IWPE 2024.
Auditing Privacy Mechanisms via Label Inference Attacks. Robert Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam Smith, Marika Swanberg. NeurIPS Spotlight 2024.
*DP-SIPS: A simpler, more scalable mechanism for differentially private partition selection. Marika Swanberg, Damien Desfontaines, Sam Haney. PETS 2023
Control, Confidentiality, and the Right to Be Forgotten. Aloni Cohen, Adam Smith, Marika Swanberg, Prashant Nalini Vasudevan. CCS 2023
Universally Composable End-to-End Secure Messaging. Ran Canetti, Mayank Varia, Palak Jain, Marika Swanberg. CRYPTO 2022
Differentially Private Sampling from Distributions. Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith, Marika Swanberg. NeurIPS 2022
*Improved Differentially Private Analysis of Variance. Ira Globus-Harris, Iris Griffith, Anna Ritz, Adam Groce, Andrew Bray. PETS 2019

News

September 2024: I am thrilled to announce that I'll be joining Google's Privacy Sandbox team as a full-time employee in December!
April 2024: I am excited to announce that I'll be a Student Researcher at Google NYC this summer with the DP+Federated team, hosted by Edo Roth, Ryan Mckenna, and Peter Kairouz!
April 2024: An ongoing work "Attaxonomy: Unpacking Differential Privacy Guarantees Against Practical Adversaries" was accepted to FORC's non-archival track! See you there!
June 2023: Our paper, Control, Confidentiality, and the Right to be Forgotten, was accepted to CCS 2023!
June 2023: Our paper, DP-SIPS was accepted to PETS 23!
March 2023: I am excited to announce that I'll be a PhD Research Intern at Google NYC this summer, hosted by Andres Muñoz Medina and Travis Dick.
January 2023: The algorithm I developed with Sam and Damien at Tumult Labs is now public!
October 2022: After much anticipation, my paper, "Control, Confidentiality, and the Right to be Forgotten" is finally live on arxiv!
September 2022: I am organizing a Boston-area student differential privacy reading group.
August 2022: I will be a visiting professor at Reed College for the Spring 2023 semester!
June 2022: My paper, "Universally Composable End-to-End Secure Messaging" was accepted to CRYPTO 2022!
May 2022: I started my position as Scientist Intern at Tumult Labs for the summer.
May 2022: I passed my qualifying exam! Officially a PhD Candidate

Invited Talks and Posters

Conferences

Talk at FORC 2024
Talk at CCS 2023
Talk at Privacy Enhancing Technologies Symposium (PETS) 2023
Lightning talk at 2022 ACM CS and Law Conference.
Lightning talk at CRYPTO 2022
Poster presentation and talk at Neural Information Processing Symposium 2021 (28% of accepted NeurIPS papers)
Talk at Privacy Enhancing Technologies Symposium 2019

Workshops

Poster at NeurIPS 2023 Workshop on Regulatable Machine Learning
Invited talk and poster at 2022 Fields Institute Workshop on Differential Privacy and Statistical Data Analysis (invite-only attendance)
Spotlight talk and poster at 2022 Updatable Machine Learning workshop (co-located with ICML)
Poster at 2022 Theory and Practice of Differential Privacy
Featured talk at 2021 Theory and Practice of Differential Privacy
Poster at 2018 Theory and Practice of Differential Privacy

Seminars (full-length)

Google Privacy in ML seminar (2024)
Google Algorithms seminar (2023)
Invited talk at Data Co-ops seminar led by Kobbi Nissim and Katrina Ligett (2023)
Invited talk at CS + Law Monthly seminar (2023)
Guest lecture on reidentification risks at BU Law course on Healthcare Decisions and Bioethics (2022)
Talk at Boston University Security seminar (2022)
Talk at Boston University Algorithns and Theory seminar (2019)
Talk at Harvard University Differential Privacy Seminar (2019)
Talk at Boston Area Differential Privacy Seminar (2020)