Úlfar Erlingsson, Vasyl Pihur, Aleksandra Korolova
2014 · arXiv
Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.
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This would be a flawed implementation. The method still works, you just have to be extremely careful to avoid exposing users through correlations when collecting anything more than a single value just once. The aforementioned RAPPOR paper[1] covers this in Section 6. [1] https://arxiv.org/abs/1407.6981
1. Any data collection at all deanonymizes the user, cf panopticlick. This isn't true. Panopticlick collects a ton of data about your browser that this proposal will not. There has been a lot of research done in this area and we know how to collect anonymous datasets. https://arxiv.org/abs/1407.6981
Google differential privacy technology, used in Chrome, starting in 2014: http://www.computerworld.com/article/2841954/googles-rappor-... https://github.com/google/rappor https://arxiv.org/abs/1407.6981 (I worked on this) I don't see any mention in the article of what algorithms Apple is using, or a link to the code. In the area of privacy, the code should really be open source, for obvious reasons.
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