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公开(公告)号：US10547592B2
公开(公告)日：2020-01-28
申请号：US15410714
申请日：2017-01-19
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Gowtham Bellala , Shagufta Mehnaz
IPC: H04L29/06
Abstract: The present disclosure discloses a method comprising: dividing, by a computing device at a first party among a plurality of parties, local data into a plurality of data segments; recursively encrypting, by the computing device, each data segment using a plurality of public keys corresponding to the plurality of parties and a mediator; sharing, by the computing device, the local data comprising the encrypted plurality of data segments with the mediator; anonymizing, by the computing device, aggregated local data received from the mediator; and communicating, by the computing device from the mediator, a global sum that preserves privacy of the plurality of parties in a multi-party environment, wherein the global sum is computed by the mediator based on the collection of data segments that are decrypted recursively using the private key corresponding to each party and the private key corresponding to the mediator.
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Patent Agency Ranking
2.发明申请公开(公告)号：US20180219842A1
公开(公告)日：2018-08-02
申请号：US15421041
申请日：2017-01-31
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Gowtham Bellala , Shagufta Mehnaz
IPC: H04L29/06
CPC classification number: H04L63/0428 , H04L67/12 , H04W4/70 , H04W12/02
Abstract: Examples disclosed herein relate to: computing, by a computing device at a party among a plurality of parties, a sum of local data owned by the party, wherein the local data is vertically partitioned into a plurality of data segments, each data segment representing a non-overlapping subset of data features; transforming a cost function of a data analytics task to a gradient descent function, wherein the cost function comprises a summation of a plurality of cost function values; initializing each data segment; anonymizing aggregated data shards received from a mediator; updating local model parameters based on the aggregated data shards; learning a global analytic model based on the updated local parameters and cost function values; and performing privacy-preserving multi-party analytics on the vertically partitioned local data based on the learned global analytic model.
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3.发明授权公开(公告)号：US10565524B2
公开(公告)日：2020-02-18
申请号：US15421144
申请日：2017-01-31
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Gowtham Bellala , Shagufta Mehnaz
Abstract: Examples disclosed herein relate to: computing, by a computing device at a party among a plurality of parties, a sum of local data owned by the party. The local data is horizontally partitioned into a plurality of data segments, with each data segment representing a non-overlapping subset of data entries owned by a particular party; computing a local gradient based on the horizontally partitioned local data; initializing each data segment; anonymizing aggregated local gradients received from the mediator, wherein the aggregated local gradients comprise gradients computed based on a plurality of data entries owned by the plurality of parties; receiving, from a mediator, a global gradient based on the aggregated local gradients; learning a global analytic model based on the global gradient; and performing privacy-preserving multi-party analytics on the horizontally partitioned local data based on the learned global analytic model.
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公开(公告)号：US10536437B2
公开(公告)日：2020-01-14
申请号：US15421041
申请日：2017-01-31
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Gowtham Bellala , Shagufta Mehnaz
Abstract: Example computing devices described herein enable computation of a machine learning model on distributed multi-party data that is vertically partitioned, in a privacy preserving fashion. The computing device computes at a party a sum of local data owned by the party, wherein the local data is vertically partitioned into a plurality of data segments, each data segment representing a non-overlapping subset of data features; transforms a cost function of a data analytics task to a gradient descent function, wherein the cost function comprises a summation of a plurality of cost function values; anonymizes aggregated data shards received from a mediator; updating local model parameters based on the aggregated data shards; and performs privacy-preserving multi-party analytics on the vertically partitioned local data based on a learned global analytic model. It leverages a secure-sum protocol that provides strong security guarantees against collusion and prior-knowledge attacks.
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5.发明申请公开(公告)号：US20180218171A1
公开(公告)日：2018-08-02
申请号：US15421144
申请日：2017-01-31
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Gowtham Bellala , Shagufta Mehnaz
Abstract: Examples disclosed herein relate to: computing, by a computing device at a party among a plurality of parties, a sum of local data owned by the party. The local data is horizontally partitioned into a plurality of data segments, with each data segment representing a non-overlapping subset of data entries owned by a particular party; computing a local gradient based on the horizontally partitioned local data; initializing each data segment; anonymizing aggregated local gradients received from the mediator, wherein the aggregated local gradients comprise gradients computed based on a plurality of data entries owned by the plurality of parties; receiving, from a mediator, a global gradient based on the aggregated local gradients; learning a global analytic model based on the global gradient; and performing privacy-preserving multi-party analytics on the horizontally partitioned local data based on the learned global analytic model.
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公开(公告)号：US20180205707A1
公开(公告)日：2018-07-19
申请号：US15410714
申请日：2017-01-19
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Gowtham Bellala , Shagufta Mehnaz
IPC: H04L29/06
CPC classification number: H04L63/0421 , H04L9/085 , H04L2209/46
Abstract: The present disclosure discloses a method comprising: dividing, by a computing device at a first party among a plurality of parties, local data into a plurality of data segments; recursively encrypting, by the computing device, each data segment using a plurality of public keys corresponding to the plurality of parties and a mediator; sharing, by the computing device, the local data comprising the encrypted plurality of data segments with the mediator; anonymizing, by the computing device, aggregated local data received from the mediator; and communicating, by the computing device from the mediator, a global sum that preserves privacy of the plurality of parties in a multi-party environment, wherein the global sum is computed by the mediator based on the collection of data segments that are decrypted recursively using the private key corresponding to each party and the private key corresponding to the mediator.
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