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Differential privacy has emerged as the main definition for private dataanalysis and machine learning. The {\em global} model of differential privacy,which assumes that users trust the data collector, provides strong privacyguarantees and introduces small errors in the output. In contrast, applicationsof differential privacy in commercial systems by Apple, Google, and Microsoft,use the {\em local model}. Here, users do not trust the data collector, andhence randomize their data before sending it to the data collector.Unfortunately, local model is too strong for several important applications andhence is limited in its applicability. In this work, we propose a frameworkbased on trusted processors and a new definition of differential privacy called{\em Oblivious Differential Privacy}, which combines the best of both local andglobal models. The algorithms we design in this framework show interestinginterplay of ideas from the streaming algorithms, oblivious algorithms, anddifferential privacy.

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors