%0 Generic %1 tang2017privacy %A Tang, Jun %A Korolova, Aleksandra %A Bai, Xiaolong %A Wang, Xueqiang %A Wang, Xiaofeng %D 2017 %K privacy %T Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12 %U http://arxiv.org/abs/1709.02753 %X 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.

@misc{tang2017privacy, 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.}, added-at = {2017-09-15T21:57:55.000+0200}, author = {Tang, Jun and Korolova, Aleksandra and Bai, Xiaolong and Wang, Xueqiang and Wang, Xiaofeng}, biburl = {https://www.bibsonomy.org/bibtex/20e8a3bf306371346efae4b25d6e992a9/dirk-rathje}, interhash = {603333ad262389d7c3b7caa105d3cd9f}, intrahash = {0e8a3bf306371346efae4b25d6e992a9}, keywords = {privacy}, note = {cite arxiv:1709.02753}, timestamp = {2017-09-15T21:57:55.000+0200}, title = {Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12}, url = {http://arxiv.org/abs/1709.02753}, year = 2017 }