Skip to content
Back to the collection

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

Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, Xiaofeng Wang

2017 · arXiv

Read the paper
HN score
0
Mentions
4
Readers
4
Months
4
What’s in a score?

From the abstract

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.

Metadata: arXiv. Abstracts retain their original authors’ rights.

In the conversation

Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels

4 selected excerpts · 4 total mentions

matthewdgreen
Neutral mention · rule
I went looking for exactly this information the other day. I was surprised to find that it's hard to come up with recent, detailed explanations of what Apple is doing for telemetry collection. When they announced their DP systems back in 2017, they were clearly doing something like Google's RAPPOR [1]. But it's been several years since then and their writeups haven't been updated very much at all [2]. This is pretty important, because these systems aren't so robust that you can just assume everything is working without review. (See, for example, this paper [3].) Apple should at least document what kinds of data are being collected, and precisely how the collection process works. [1] https://static.googleusercontent.com/media/research.google.c... [2] https://www.apple.com/privacy/docs/Differential_Privacy_Over... [3] https://arxiv.org/pdf/1709.02753
Cynddl
Neutral mention · rule
Differential privacy appears regularly on Hacker News, with either theoretical articles or projects that aim to implement it. Yet often there is a huge gap between both. For example, Apple has touted about its use of differential privacy, but researchers [1] have shown that the privacy budget is reset every day and the parameters, buried inside the code, lack a proper derivation. Similarly, Uber seems to use DP for internal analytics. However, the proposed model does not seem really robust and does not provide accurate results at all [2]. One should always carefully review claims associated with implementations of differential privacy. [1] https://arxiv.org/abs/1709.02753 [2] https://github.com/frankmcsherry/blog/blob/master/posts/2018...
frankmcsherry
Neutral mention · rule
> Wired recently deployed a team of experts to deconstruct Apple’s code and found its differential privacy practices to be lacking, a characterization that the company strongly disputes. If by "deployed" they mean "interviewed about their work". I don't understand Time crediting Wired with the research[0], which does a disservice to their actual sources of funding. https://arxiv.org/abs/1709.02753
pgeorgi
Neutral mention · rule
Apple still needs data to build these locally running models. They advertise differential privacy as a solution for that but researchers aren't impressed (https://arxiv.org/pdf/1709.02753.pdf). Meanwhile on Android you can opt for a different photo gallery app, giving Google _nothing_ to work with, while on iOS you're stuck with whatever Apple allows you to use.

Keep following the thread.

More in Security & cryptography

More from the same topics.