Google Inc.

Microsoft Technology Licensing, LLC

Mariana Raykova

Seny Fakaba Kamara

Sarvar M Patel

Karn Seth

Kevin Yeo

Giuseppe Persiano

Gang Wang

Benjamin Kreuter

Kevin Wei Li Yeo

Marcel M Moti Yung

Philip Andrew McDonnell

Amin Charaniya

Marcel M M Yung

Evgeny Skvortsov

Craig Wright

Eli Simon Fox-Epstein

Srinivasan Seshadri

Tancrède Lepoint

Ananth Raghunathan

Margo Narayan

Craig William Wright

Systems and methods for detecting a collision when combining a first encrypted data structure and a second encrypted data structure are disclosed. The system can receive the first encrypted data structure representative of a first plurality of registers. Each register in the first plurality of registers can have an encrypted fingerprint value, and an encrypted register identifier value. The system can receive the second encrypted data structure representative of a second plurality of registers. The system can calculate a first sum associated with a first register of the first plurality of registers based on the fingerprint value of the first register. The system can calculate a second sum associated with a second register of the second plurality of registers. The system can determine a validity bit associated with the collision based on a comparison of the first sum and the second sum.

Cryptographic computation techniques for multi-party reach and frequency

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for retrieving information from a server. Methods can include a server receiving a set of client-encrypted queries. The server identifies a set of server-encrypted decryption keys and transmits the set to the client device. The server receives a set of client-server-encrypted decryption keys that includes the set of server-encrypted decryption keys encrypted by the client device. The server also receives a set of client-encrypted/client-derived decryption keys that were derived by the client device. The server generates matching a map that specifies matches between the set of client-server-encrypted decryption keys and the set of client-encrypted/client-derived decryption keys. The server filters the set of client-encrypted queries using the map to create a set of filtered client-encrypted queries and generates a set of query results.

Multi-key information retrieval

This document relates to using secure MPC to select digital components in ways that preserve user privacy and protects the security of data of each party that is involved in the selection process. In one aspect, a method includes receiving, by a first computing system of a secure MPC system and from a client device, a digital component request and a nonce. The first computing system generates, based on the nonce and a function, an array including a share of a Bloom filter representing user group identifiers for user groups that include a user of the client device as a member. For each of multiple user group identifiers, the first computing system calculates, in collaboration with one or more second computing systems of the secure MPC system and using the array, a respective first secret share of one or more user group membership condition parameters.

Enhanced performance of secure multi-party computation

A method includes executing an instruction to execute a query for a data block, the data block associated with a corresponding memory level of a logarithmic number of memory levels (l) of memory, each memory level (l) including physical memory (RAM) residing on memory hardware of a distributed system. The method also includes retrieving a value associated with the data block from an oblivious hash table using a corresponding key, and extracting un-queried key value pairs from the oblivious hash table associated with un-queried data blocks after executing a threshold number of queries for data blocks. The method also includes a multi-array shuffle routine on the extracted key value pairs from the oblivious hash table to generate an output array containing the un-queried key value pairs.

Oblivious RAM with logarithmic overhead

A method including at each of a number of client devices receiving a data item, receiving a public key from a second computing system, encrypting the data item using the public key to produce a singly encrypted data item, engaging in an oblivious pseudorandom function protocol with a first computing system using the singly encrypted data item to produce a seed, generating an encrypted secret share using a threshold secret sharing function under which the encrypted secret share cannot be decrypted until a threshold number of encrypted secret shares associated with the same singly encrypted data item are received, and transmitting the encrypted secret share to the first computing system and at the first computing system receiving a number of encrypted secret shares from the number of client devices, processing the number of encrypted secret shares to produce processed data, and transmitting the processed data to a second computing system.

Methods for protecting privacy

A method disclosed herein may include receiving, at a first computing system, encrypted identifiers and encrypted values, performing, by the first computing system, a concealing operation on the encrypted identifiers to produce concealed encrypted identifiers, wherein the concealing operation conceals the encrypted identifiers from the first computing system and a second computing system but enables matching between the concealed encrypted identifiers, decrypting, by the second computing system, the concealed encrypted identifiers to produce concealed identifiers, and performing, by the second computing system, an aggregation operation using the concealed identifiers and the encrypted values to produce an encrypted aggregate value without accessing personally identifiable information associated with the encrypted values.

Methods for aggregating credit across interactions

A server module evaluates a circuit based on concealed inputs provided by respective participant modules, to provide a concealed output. By virtue of this approach, no party to the transaction (including the sever module) discovers any other party's non-concealed inputs. In a first implementation, the server module evaluates a garbled Boolean circuit. This implementation also uses a three-way oblivious transfer technique to provide a concealed input from one of the participant modules to the serer module. In a second implementation, the server module evaluates an arithmetic circuit based on ciphertexts that have been produced using a fully homomorphic encryption technique. This implementation modifies multiplication operations that are performed in the evaluation of the arithmetic circuit by a modifier factor; this removes bounds placed on the number of the multiplication operations that can be performed.

Secure computation using a server module

A server module evaluates a circuit based on concealed inputs provided by respective participant modules, to provide a concealed output. By virtue of this approach, no party to the transaction (including the sever module) discovers any other party's non-concealed inputs. In a first implementation, the server module evaluates a garbled Boolean circuit. This implementation also uses a three-way oblivious transfer technique to provide a concealed input from one of the participant modules to the server module. In a second implementation, the server module evaluates an arithmetic circuit based on ciphertexts that have been produced using a fully homomorphic encryption technique. This implementation modifies multiplication operations that are performed in the evaluation of the arithmetic circuit by a modifier factor; this removes bounds placed on the number of the multiplication operations that can be performed.

Shares for one or more data values in a dataset can be computed using evaluation point values and sharing polynomials. Lagrangian coefficients can also be computed for the evaluation point values. The shares and the Lagrangian coefficients may be used to evaluate the polynomials on the data values. The technique can also include encrypting the Lagrangian coefficients according to an encryption scheme that provides for addition operations between encrypted values. An operation on representations of coefficients of the evaluation polynomial, representations of the shares, and the encrypted representations of the Lagrangian coefficients can be delegated to a remote computing environment. The operation can be performed at the remote computing environment, such as by performing a map-reduce operation. Results of the delegated operation can be received from the remote computing environment and processed to produce representation(s) of evaluation(s) of the polynomial on the data value(s).

Polynomial evaluation delegation

This document describes techniques and apparatuses for secure computing in multi-tenant data centers. These techniques permit a client to delegate computation of a function to multiple physical computing devices without the client's information being vulnerable to exposure. The techniques prevent discovery of the client's information by a malicious entity even if that entity is a co-tenant on many of the same physical computing devices as the client.

Secure computing in multi-tenant data centers

Counting values can be encrypted as a set of counting value cyphertexts according to a hidden vector encryption scheme using sample values of a set of samples, where each of the samples can include multiple sample values. Additionally, tokens can be generated. The tokens can be configured according to the hidden vector encryption scheme, such that each of the tokens can enable decryption of matching cyphertexts. Processing of the counting value cyphertexts and the tokens can be delegated to a map-reduce computer cluster. The cluster can run a map-reduce program to produce and return count representations. Each count representation can represent a count of a set of the counting value cyphertext(s) whose decryption was enabled by one or more of the token(s). For example, the counts may be counts that can be used in constructing a data structure such as a decision tree.

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New York, NY, United States of America

Mariana Raykova