Robust Input Verification for Secure Multi-Party Computation (MPC) with Clients

    公开(公告)号:US20230050494A1

    公开(公告)日:2023-02-16

    申请号:US17966497

    申请日:2022-10-14

    申请人: VMware, Inc.

    IPC分类号: H04L9/08 H04L9/32 H04L9/00

    摘要: In one set of embodiments, each server executing a secure multi-party computation (MPC) protocol can receive shares of inputs to the MPC protocol from a plurality of clients, where each input is private to each client and where each share is generated from its corresponding input using a threshold secret sharing scheme. Each server can then verify whether the shares of the plurality of inputs are valid/invalid and, for each invalid share, determine whether a client that submitted the invalid share or a server that holds the invalid share is corrupted. If the client that submitted the invalid share is corrupted, each server can ignore the input of that corrupted client during a computation phase of the MPC protocol. Alternatively, if the server that holds the invalid share is corrupted, each server can prevent that corrupted server from participating in the computation phase.

    Robust Input Verification for Secure Multi-Party Computation (MPC) with Clients

    公开(公告)号:US20220069979A1

    公开(公告)日:2022-03-03

    申请号:US17010526

    申请日:2020-09-02

    申请人: VMware, Inc.

    IPC分类号: H04L9/08 H04L9/00 H04L9/32

    摘要: In one set of embodiments, each server executing a secure multi-party computation (MPC) protocol can receive shares of inputs to the MPC protocol from a plurality of clients, where each input is private to each client and where each share is generated from its corresponding input using a threshold secret sharing scheme. Each server can then verify whether the shares of the plurality of inputs are valid/invalid and, for each invalid share, determine whether a client that submitted the invalid share or a server that holds the invalid share is corrupted. If the client that submitted the invalid share is corrupted, each server can ignore the input of that corrupted client during a computation phase of the MPC protocol. Alternatively, if the server that holds the invalid share is corrupted, each server can prevent that corrupted server from participating in the computation phase.

    System and method for anonymous message broadcasting

    公开(公告)号:US11102179B2

    公开(公告)日:2021-08-24

    申请号:US16748571

    申请日:2020-01-21

    申请人: VMware, Inc.

    IPC分类号: H04L29/06 H04L9/08

    摘要: A system and method for anonymous message broadcasting uses secret shares of a first vector of size i and a second vector of size j from each client device with a message in an anonymity set of client devices. Each secret share of the first and second vectors is received at each of a plurality of message broadcasting servers to construct a matrix M of i and j dimensions, which is added to a matrix A of i and j dimensions maintained at that message broadcasting server. The matrix A at each message broadcasting server is shared with the other message broadcasting servers and a final matrix A is constructed using the shared matrices A at each message broadcasting server, wherein the final matrix A includes the messages from the client devices in the anonymity set. The messages in the final matrix A are broadcasted from the message broadcasting servers.

    TRAFFIC REDUNDANCY DEDUPLICATION FOR BLOCKCHAIN RECOVERY

    公开(公告)号:US20230205738A1

    公开(公告)日:2023-06-29

    申请号:US17562684

    申请日:2021-12-27

    申请人: VMware, Inc.

    IPC分类号: G06F16/174

    CPC分类号: G06F16/1752

    摘要: In some embodiments, a method receives data for a block in a blockchain during a recovery process in which a recovering replica is recovering the block for a first instance of the blockchain being maintained by the recovering replica. The block is received from a second instance of the blockchain being maintained by a source replica. The method splits the data for the block into a plurality of chunks. Each chunk includes a portion of the data for the block; It is determined whether the recovering replica can recover a chunk in the plurality of chunks using a representation of the chunk. In response to determining that the recovering replica can recover the chunk, sending the representation of the chunk to the recovering replica. In response to determining that the recovering replica cannot recover the chunk, sending the data for the chunk to the recovering replica.

    Efficient Three-Party Private Set Intersection (PSI)

    公开(公告)号:US20230102423A1

    公开(公告)日:2023-03-30

    申请号:US17487547

    申请日:2021-09-28

    申请人: VMware, Inc.

    发明人: Avishay Yanai

    IPC分类号: H04L9/08 H04L9/14

    摘要: Techniques for implementing efficient three-party private set intersection (PSI) are provided. In one set of embodiments these techniques make use of an oblivious key-value store (OKVS), which is a cryptographic data structure that encodes a set of key-value pairs ({ki, vi}) and exhibits the following properties: (A) if a receiver decodes the OKVS on some input q=kj, the output will be vj, and (B) the receiver cannot tell, from the outputs generated by the OKVS, what keys (i.e., ki's) are encoded. By using an OKVS, the techniques of the present disclosure can achieve three-party PSI in a manner that is more efficient and scalable than existing protocols.

    FEDERATED INFERENCE
    8.
    发明申请

    公开(公告)号:US20220101189A1

    公开(公告)日:2022-03-31

    申请号:US17039294

    申请日:2020-09-30

    申请人: VMware, Inc.

    摘要: In one set of embodiments, a computer system can receive a query data instance for which a prediction is requested and transmit the query data instance to a plurality of computing nodes. The computer system can further receive, from each computing node, a per-node prediction for the query data instance, where the per-node prediction is generated by the computing node using a trained version of a local machine learning (ML) model of the computing node and where the per-node prediction is encrypted in a manner that prevents the query server from learning the per-node prediction. The computer system can then aggregate the per-node predictions, generate a federated prediction based on the aggregated per-node predictions, and output the federated prediction as a final prediction result for the query data instance.