Microsoft Technology Licensing, LLC

Janardhan Dattatreya Kulkarni

Sergey M Yekhanin

Joshua Stanley Allen

Bolin Ding

Ankit Srivastava

Ralph Kennedy Johnston, Jr

Josh D Benaloh

Jacob Rubin Lorch

Dheepak Ramaswamy

Muthukrishnan Paramasivam

Melissa Erin Chase

Sreeram Nivarthi

Srinath T V Setty

Harsha Prasad Nori

Ananthatejas Raghavan

Olga Ohrimenko

Aleksey Ashikhmin

Sivakanth Gopi

Jagadeesh Virupaksha Huliyar

Anjaneya Malpani

Sriradha Selvaraj

Alexander Norton Meade

Xinyun Sun

Bingran Luo

Ralph Kennedy Johnston

A secure enclave may be used to satisfy privacy requirements and audit requirements. Code may be loaded into the secure enclave. The code may generate a predefined report based on data and added noise. The pre-defined report may be subject to audit requirements. The data may be subject to the privacy requirements. The secure enclave may generate an encryption key and a decryption key based on the code. Only the secure enclave may have access to the decryption key. And the secure enclave may allow only a verified copy of the code to access the decryption key. With the added noise, the report may satisfy a pre-defined differential privacy guarantee. Encrypting the code and ensuring that the report satisfies the differential privacy guarantee may satisfy the privacy requirements. Retaining the report, the code, the secure enclave, and the encrypted data may satisfy the audit requirements.

Using a secure enclave to satisfy retention and expungement requirements with respect to private data

A system and method for applying noise to data is described. The system accesses a metric value of a metric of each user from a group of users of an application. The metric indicates a measure of an operation of the application by a corresponding user. The system generates noise values and defines a distribution of the noise values to the group of users. The system modifies the metric value of the metric of each user with a corresponding noise value from the noise values based on the distribution.

Noise generation for differential privacy

This document relates to hardware protection of differential privacy techniques. One example obtains multiple instances of encrypted telemetry data within a secure enclave and processes the encrypted telemetry data to obtain multiple instances of unencrypted telemetry data. The example also processes, within the secure enclave, the multiple instances of unencrypted telemetry data to obtain a perturbed aggregate. The example also releases the perturbed aggregate from the secure enclave.

Hardware protection for differential privacy

A facility for reporting on original values on behalf of each of a plurality of users&#x2014;each falling within a domain partitioned into a plurality of segments of uniform size&#x2014;is provided. For each of the plurality of users, the facility (a) randomly selects an upward-rounding window size that is smaller than the segment size; (b) for each source value in a domain, randomly determines a mapping of each segment of the domain to a segment identifier value; (c) determines an original value for the user; (d) adds the upward-rounding window size to the user's original value to obtain a window-augmented original value; (e) identifies a segment containing the window-augmented original value; (f) identifies a segment identifier value mapped-to from the identified segment using the determined mapping; and (g) transmits a reporting communication on the user's behalf reporting a value based on the identified segment identifier value.

Collection of sensitive data&#x2014;such as software usage data or other telemetry data&#x2014;over repeated collection cycles in satisfaction of privacy guarantees

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Redmond, WA, United States of America

Janardhan Dattatreya Kulkarni