# Preserving Privacy in Personalized Models for Distributed Mobile Services

arXiv: https://arxiv.org/abs/2101.05855 · HTML: https://mlsanity.com/paper/2101.05855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks](https://mlsanity.com/paper/1911.07135/) (2019)
- [MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples](https://mlsanity.com/paper/1909.10594/) (2019)
- [Chiron: Privacy-preserving Machine Learning as a Service](https://mlsanity.com/paper/1803.05961/) (2018)
- [Defending Model Inversion and Membership Inference Attacks via Prediction Purification](https://mlsanity.com/paper/2005.03915/) (2020)
- [Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy](https://mlsanity.com/paper/1802.01186/) (2018)
- [DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision](https://mlsanity.com/paper/1802.02147/) (2018)
- [Privacy-preserving Machine Learning through Data Obfuscation](https://mlsanity.com/paper/1807.01860/) (2018)
- [Generating Artificial Data for Private Deep Learning](https://mlsanity.com/paper/1803.03148/) (2018)
- [Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network](https://mlsanity.com/paper/1701.03578/) (2017)
- [Empirical Characterization of Mobility of Multi-Device Internet Users](https://mlsanity.com/paper/2003.08512/) (2020)
- [Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices](https://mlsanity.com/paper/1501.02484/) (2015)

## Built on by (most cited first, 2 of 2)

- [SODA: Protecting Proprietary Information in On-Device Machine Learning Models](https://mlsanity.com/paper/2312.15036/) (2023)
- [SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models](https://mlsanity.com/paper/2503.03160/) (2025)

## Abstract (arXiv)

The ubiquity of mobile devices has led to the proliferation of mobile services that provide personalized and context-aware content to their users. Modern mobile services are distributed between end-devices, such as smartphones, and remote servers that reside in the cloud. Such services thrive on their ability to predict future contexts to pre-fetch content or make context-specific recommendations. An increasingly common method to predict future contexts, such as location, is via machine learning (ML) models. Recent work in context prediction has focused on ML model personalization where a personalized model is learned for each individual user in order to tailor predictions or recommendations to a user's mobile behavior. While the use of personalized models increases efficacy of the mobile service, we argue that it increases privacy risk since a personalized model encodes contextual behavior unique to each user. To demonstrate these privacy risks, we present several attribute inference-based privacy attacks and show that such attacks can leak privacy with up to 78% efficacy for top-3 predictions. We present Pelican, a privacy-preserving personalization system for context-aware mobile services that leverages both device and cloud resources to personalize ML models while minimizing the risk of privacy leakage for users. We evaluate Pelican using real world traces for location-aware mobile services and show that Pelican can substantially reduce privacy leakage by up to 75%.