2016

Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy

Nguyên, Thông T., Xiao, Xiaokui, Yang, Yin et al.

Understand

Organizations with a large user base, such as Samsung and Google, can potentially benefit from collecting and mining users' data.

However, doing so raises privacy concerns, and risks accidental privacy breaches with serious consequences.
Local differential privacy (LDP) techniques address this problem by only collecting randomized answers from each user, with guarantees of plausible deniability; meanwhile, the aggregator can still build accurate models and predictors by analyzing large amounts of such randomized data.
So far, existing LDP solutions either have severely restricted functionality, or focus mainly on theoretical aspects such as asymptotical bounds rather than practical usability and performance.

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