Snap Inc.

Zoom Communications, Inc.

Antonio Marcedone

Subhash Sankuratripati

Vasyl Pihur

Dachuan Huang

Ruogu Zeng

Frederick Liu

Balachandar Ganesh Kesavan

Selective access to an encrypted conversation history can be granted as described herein. For example, a first client device can determine a message key used to encrypt messages associated with a conversation. The first client device can then generate a key ciphertext by: determining a prior secret key, determining a current secret key by hashing the prior secret key and the message key, and generating the key ciphertext by encrypting the prior secret key and the message key using the current secret key. The first client device can then transmit the key ciphertext to a server system. To grant the second client device with access to the encrypted messages, the first client device can transmit the current secret key to the second client device, which can obtain the key ciphertext from the server system and apply the current secret key to derive the message key and decrypt the messages.

Granting selective access to an encrypted conversation history

Methods, computer readable media, devices, and systems provide for distributed machine learning. In one aspect, a method of training a model is disclosed. The method includes receiving, by a client device, from one or more servers, an intermediate model, training, by the client device, the intermediate model based on private data, and transmitting, by the client device, to the one or more servers, the trained intermediate model.

Distributed machine learning for improved privacy

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New York, NY, United States of America

Antonio Marcedone