mlsanityData to 7 Oct 2026

Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations

arXiv 2310.19978 ↗First posted 30 Oct 2023cs.LG stat.CO stat.MLcited by 8

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Abstract (arXiv)

To the best of our knowledge, there are no methods today for training differentially private regression models on sparse input data. To remedy this, we adapt the Frank-Wolfe algorithm for L1 penalized linear regression to be aware of sparse inputs and to use them effectively. In doing so, we reduce the training time of the algorithm from O( T D S + T N S) to O(N S + T √D logD + T S2), where T is the number of iterations and a sparsity rate S of a dataset with N rows and D features. Our results demonstrate that this procedure can reduce runtime by a factor of up to 2,200×, depending on the value of the privacy parameter ε and the sparsity of the dataset.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 30 Oct 2023.

Builds on

17 of 40 references.

References · 17

  1. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  2. 2021Private Stochastic Convex Optimization: Optimal Rates in ℓ1 Geometrycited by 83
  3. 2019Diffprivlib: The IBM Differential Privacy Librarycited by 51
  4. 2021Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Timecited by 51
  5. 2021High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Datacited by 42
  6. 2020LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scalecited by 26
  7. 2022A General Framework for Auditing Differentially Private Machine Learningcited by 25
  8. 2021Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structurescited by 19
  9. 2021Frank-Wolfe and friends: a journey into projection-free first-order optimization methodscited by 10
  10. 2021Heavy Ball Momentum for Conditional Gradientcited by 7
  11. 2023The Challenge of Differentially Private Screening Rulescited by 6
  12. 2019Differential Privacy for Sparse Classification Learningcited by 5
5 more
  1. 2019A Knowledge Transfer Framework for Differentially Private Sparse Learningcited by 5
  2. 2019Differentially Private Empirical Risk Minimization with Sparsity-Inducing Normscited by 4
  3. 2014A Back-to-Basics Empirical Study of Priority Queuescited by 3
  4. 2023Sparse Private LASSO Logistic Regressioncited by 3
  5. 2023Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profilecited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 1.

8 papers.

  1. 2024SoK: A Review of Differentially Private Linear Models For High-Dimensional Datacited by 5
  2. 2024High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updatescited by 3
  3. 2024Feature Selection from Differentially Private Correlationscited by 2
  4. 2025Differentially Private Iterative Screening Rules for Linear Regressioncited by 2
  5. 2024Better Locally Private Sparse Estimation Given Multiple Samples Per Usercited by 1
  6. 2024More Options for Prelabor Rupture of Membranes, A Bayesian Analysiscited by 1
  7. 2025Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attackcited by 1
  8. 2026Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complicationscited by 0