Microsoft Technology Licensing, LLC

John Wernsing

Kristin E Lauter

Ran Gilad-Bachrach

Kim Laine

Michael Naehrig

Nathan Dowlin

John Carlton Platt

Jonathan David Goldstein

Badrish Chandramouli

Michael Barnett

The techniques and/or systems described herein are directed to improvements in homomorphic encryption to improve processing speed and storage requirements. For example, the techniques and/or systems can be used on a client device to encode data to be sent to a remote server, to be operated on while maintaining confidentiality of data. For example, data including a real number can be encoded as a polynomial, with the fractional part of the real number encoded as high-order coefficients in the polynomial. Further, real numbers can be approximated and encoded in a polynomial using a fractional base, and/or the encoding can include slot encoding. Thus, the optimized encodings disclosed herein provide an optimized homomorphic encryption scheme.

Homomorphic encryption with optimized encoding

The techniques and/or systems described herein are directed to improvements in homomorphic encryption to improve processing speed and storage requirements. For example, the techniques and/or systems can be used on a client device to encode data to be sent to a remote server, to be operated on while maintaining confidentiality of data. The encoding scheme can be optimized by automatically selecting one or more parameters using an error growth simulator based on an actual program that operates on the encoded data. For example, the simulator can be used iteratively to determine an optimized parameter set which allows for improved homomorphic operations while maintaining security and confidentiality of a user's data.

Homomorphic encryption with optimized parameter selection

The techniques and/or systems described herein are directed to improvements in homomorphic operations within a homomorphic encryption scheme. The homomorphic operations may be performed on encrypted data received from a client device without decrypting the data at a remote computing device, thereby maintaining the confidentiality of the data. In addition to the operations of addition, subtraction, and multiplication, the homomorphic operations may include an approximate division, a sign testing, a comparison testing, and an equality testing. By combining these operations, a user may perform optimized operations with improved processor and memory requirements.

Homomorphic encryption with optimized homomorphic operations

Some examples include high-performance query processing of real-time and offline temporal-relational data. Further, some implementations include processing streaming data events by annotating individual events with a first timestamp (e.g., a 'sync-time') and second timestamp that may identify additional event information. The stream of incoming data events may be organized into a sequence of data batches that each include multiple data events. The individual data batches in the sequence may be processed in a non-decreasing 'sync-time' order.

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John Wernsing