Differential Privacy — Under the Hood
Apple released a little more information about #DifferentialPrivacy with a paper and an excellent blog post (°). A quick refresher - Under differential privacy, you • Collect user’s data (locally, or on the server) • Add noise to mask the data, but in such a way that when you • Average out the data, the noise averages out, and meaningful information emerges The trick, of course, is in the details, at the very least, you need to be careful in • calibrating the noise so that it averages out (algorithms matter!) • collecting and transmitting information in a manner that protects privacy (secure storage! encryption!) • keeping the information under privacy exposure thresholds (don’t combine data across different use cases! restrict the number of use cases!) etc. It's a fascinating read, and, I suspect, the very beginning of the field. I expect signal processing (Huffman coding, etc) to come into play any day now (°) Learning with Privacy at Scale - the paper - h...