mlsanityData to 7 Oct 2026

Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12

arXiv 1709.02753 ↗First posted 8 Sep 2017cs.CR cs.CY cs.LGcited by 42

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Abstract (arXiv)

In June 2016, Apple announced that it will deploy differential privacy for some user data collection in order to ensure privacy of user data, even from Apple. The details of Apple's approach remained sparse. Although several patents have since appeared hinting at the algorithms that may be used to achieve differential privacy, they did not include a precise explanation of the approach taken to privacy parameter choice. Such choice and the overall approach to privacy budget use and management are key questions for understanding the privacy protections provided by any deployment of differential privacy. In this work, through a combination of experiments, static and dynamic code analysis of macOS Sierra (Version 10.12) implementation, we shed light on the choices Apple made for privacy budget management. We discover and describe Apple's set-up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple's servers. We find that although Apple's deployment ensures that the (differential) privacy loss per each datum submitted to its servers is 1 or 2, the overall privacy loss permitted by the system is significantly higher, as high as 16 per day for the four initially announced applications of Emojis, New words, Deeplinks and Lookup Hints. Furthermore, Apple renews the privacy budget available every day, which leads to a possible privacy loss of 16 times the number of days since user opt-in to differentially private data collection for those four applications. We advocate that in order to claim the full benefits of differentially private data collection, Apple must give full transparency of its implementation, enable user choice in areas related to privacy loss, and set meaningful defaults on the privacy loss permitted.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Sep 2017.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 8; 2024: 7; 2025: 8; 2026: 1.

42 papers.

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  1. 2021Robust and Differentially Private Mean Estimationcited by 66
  2. 2021High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Datacited by 42
  3. 2021Generalized Linear Bandits with Local Differential Privacycited by 29
  4. 2021Differentially private inference via noisy optimizationcited by 27
  5. 2022DP-PCA: Statistically Optimal and Differentially Private PCAcited by 24
  6. 2022How unfair is private learning ?cited by 18
  7. 2021Faster Rates of Private Stochastic Convex Optimizationcited by 12
  8. 2021Privately Publishable Per-instance Privacycited by 12
  9. 2023Near Optimal Private and Robust Linear Regressioncited by 9
  10. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
  11. 2023Optimal Locally Private Nonparametric Classification with Public Datacited by 7
  12. 2021An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprisescited by 6
30 more
  1. 2023Mean Estimation Under Heterogeneous Privacy Demandscited by 6
  2. 2023The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learningcited by 5
  3. 2022Algorithms with More Granular Differential Privacy Guaranteescited by 4
  4. 2023Improved Analysis of Sparse Linear Regression in Local Differential Privacy Modelcited by 4
  5. 2022LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximationcited by 3
  6. 2023TraVaG: Differentially Private Trace Variant Generation Using GANscited by 3
  7. 2025Setting varepsilon is not the Issue in Differential Privacycited by 3
  8. 2021Effective and Privacy preserving Tabular Data Synthesizingcited by 2
  9. 2022How Much User Context Do We Need? Privacy by Design in Mental Health NLP Applicationcited by 2
  10. 2024An Algorithm for Streaming Differentially Private Datacited by 2
  11. 2024Enhancing Learning with Label Differential Privacy by Vector Approximationcited by 2
  12. 2025Private Continual Counting of Unbounded Streamscited by 2
  13. 2021Smoothed Differential Privacycited by 1
  14. 2021A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity indexcited by 1
  15. 2022Differentially Private Learning of Hawkes Processescited by 1
  16. 2022On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Datacited by 1
  17. 2023Ranking Differential Privacycited by 1
  18. 2024Beyond Statistical Estimation: Differentially Private Individual Computation via Shufflingcited by 1
  19. 2024Distribution-Aware Mean Estimation under User-level Local Differential Privacycited by 1
  20. 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicalitycited by 1
  21. 2025Differentially Private Sparse Linear Regression with Heavy-tailed Responsescited by 1
  22. 2025An Interactive Framework for Finding the Optimal Trade-off in Differential Privacycited by 1
  23. 2025Cocoon: A System Architecture for Differentially Private Training with Correlated Noisescited by 1
  24. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  25. 2022Efficient Private SCO for Heavy-Tailed Data via Averaged Clippingcited by 0
  26. 2024Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noisecited by 0
  27. 2024Testing Credibility of Public and Private Surveys through the Lens of Regressioncited by 0
  28. 2025Locally Private Nonparametric Contextual Multi-armed Banditscited by 0
  29. 2025Releasing Differentially Private Event Logs Using Generative Modelscited by 0
  30. 2025Non-Asymptotic Analysis of Online Local Private Learning with SGDcited by 0