Lemon Inc.

Beijing Jingdong Shangke Information Technology Co., Ltd.

Beijing Jingdong Century Trading Co., Ltd.

Qiang Yan

Jian Du

Bo Jiang

Haohao Qian

Yongjun Zhao

Sagar Sharma

Chuyuan Chen

Xiao Wang

Aihua Li

Shengli Ge

Differential privacy composition determination in a secure communication is provided. A method for determining differential privacy composition includes determining a differential privacy configuration including a first privacy parameter and a second privacy parameter, determining a privacy loss distribution, and performing a partial convolution operation of the privacy loss distribution by transforming the privacy loss distribution based on a predetermined parameter, determining an integral range based on the first privacy parameter and the privacy loss distribution, transforming the privacy loss distribution based on the integral range, and performing the partial convolution operation based on the integral range. The method also includes determining the second privacy parameter based on a result of the partial convolution operation, and controlling a dataset based on the differential privacy configuration to limit access to the dataset.

Transformed partial convolution algorithm for composition determination

Methods and systems for secure computation and communication are provided. The method includes transforming identifications of a first dataset using a first transforming scheme, and transforming attributes of the first dataset using a second transforming scheme. The method also includes dispatching the transformed first dataset, receiving a second dataset, transforming identifications of the received second dataset, dispatching the identifications of the transformed received second dataset, and receiving a set of identifications. The method further includes generating a first intersection of the received set of identifications and the transformed received second dataset, generating a first share based on the first intersection, receiving a second share, and constructing a result based on the first share and the second share.

Secure computation and communication

Modelling for efficient resource allocation and/or distribution in secure computation and communication for differential privacy protocols and/or algorithms is provided. A method for allocating a resource of a differentially private system in secure computation and communication includes aggregating attributes from a usage log of the differentially private system for a predetermined period of time, generating a moving aggregation based on the aggregated attributes, training a machine learning model based on the aggregated attributes and the moving aggregation, predicting a distribution of the resource using the trained machine learning model, and allocating the resource based on the predicted distribution. The resource includes a differential privacy parameter.

Resource allocation in secure computation and communication

Dynamic allocation and/or distribution of a resource in secure computation and communication to achieve a desired level of data privacy protection is provided. A method for dynamically allocating a resource of a differentially private system in secure computation and communication includes aggregating attributes from a usage log of the differentially private system for a predetermined period of time, generating a moving aggregation based on the aggregated attributes, optimizing a first attribute of the attributes of the usage log based on the moving aggregation and a set of constraints, generating a distribution of the resource based on the optimized first attribute, and dynamically allocating the resource based on the generated distribution. The resource includes a differential privacy parameter.

Dynamic resource allocator in secure computation and communication

Protecting membership in secure multi-party computation and communication is provided. A method of protecting membership includes generating a padding dataset, up-sampling a first dataset with the padding dataset, transforming and dispatching the first dataset, receiving a second dataset, and performing a private set intersection operation based on the first dataset and the second dataset to generate a third dataset. Each of the first dataset, the padding dataset, and/or the second dataset includes one or more personal identification information for each user or member in the dataset.

Protecting membership in a secure multi-party computation and/or communication

Protecting data privacy in secure multi-party computation and communication is provided. A method of protecting data privacy includes determining a differential privacy configuration, determining a number of iterations based on a first parameter and a second parameter, and for each of the number of iterations generating a random value and a random noise data; generating a first message and a second message; and performing a transfer based on the first message, the second message, and an input data to output one of the first message and the second message. The method also includes generating a first noise data based on the random noise data in each of the number of iterations, generating a first share based on a first dataset and a second dataset, applying the first noise data to the first share, and constructing a result based on the first share and a second share.

Secure multi-party computation and communication

Protecting membership and data in secure multi-party computation and communication is provided. A method of protecting membership and data includes generating a padding dataset. A size of the padding dataset is determined based on a data privacy configuration. The method also includes up-sampling a first dataset with the padding dataset, transforming the first dataset, dispatching the first dataset, performing an intersection operation based on the first dataset and a second dataset to generate a third dataset, generating a first share based on the third dataset, and constructing a result based on the first share and a second share.

Protecting membership and data in a secure multi-party computation and/or communication

Methods and systems for protecting membership privacy for secure computation and communication are provided. The method includes providing a first dataset, determining a number N based on a data privacy configuration, and generating a padding dataset having more than N elements. An intersection of the padding dataset and the first dataset is empty. The method also includes shuffling the padding dataset, up-sampling the first dataset with a first N elements of the shuffled padding dataset, and performing an intersection operation based on the up-sampled first dataset and a received dataset.

Protecting membership for secure computation and communication

Differential privacy composition determination in secure computation and communication of a dataset is provided. A method for differential privacy composition determination includes determining a differential privacy configuration that includes a first privacy parameter and a second privacy parameter, determining a privacy loss distribution, and providing a number of composition operations. The method also includes determining a third privacy parameter and a fourth privacy parameter for a differential privacy composition based on the differential privacy configuration, the privacy loss distribution, and the number of composition operations. The method further includes controlling the dataset based on at least one of the third privacy parameter and the fourth privacy parameter.

Fast convolution algorithm for composition determination

Methods and systems for protecting membership privacy in multi-identification secure computation and communication are provided. The method includes providing a dataset having a first set of membership identifications and a second set of membership identifications, determining a number N based on a data privacy configuration, generating and shuffling a first padding dataset, and up-sampling the first set of membership identifications with a first N elements of the shuffled first padding dataset. The method also includes inserting a first N random membership-identification elements to the second set of membership identifications, generating and shuffling a second padding dataset, up-sampling the inserted second set of membership identifications with a first N elements of the shuffled second padding dataset. The method further includes performing an intersection operation based on the up-sampled dataset and a received dataset.

Protecting membership in multi-identification secure computation and communication

Methods and systems for secure computation and communication are described herein. The method includes transforming identifications of a first dataset using a transforming scheme, dispatching the transformed identifications of the first dataset, receiving identifications of a second dataset, transforming the identifications of the second dataset, dispatching the transformed identifications of the second dataset, receiving a set of identifications, generating a first intersection of the received set of identifications and the transformed identifications of second dataset, and determining a first permutation based on the first intersection. The method also includes performing an oblivious shuffling based on the first permutation and a set of attributions to generate a first share. A size of the first share is the same as a size of the first intersection. The method further includes receiving a second share and constructing a first result based on the first share and the second share.

A classification method and a classification device for service data. The classification method includes: acquiring service data, each including plural service indicators; extracting the service data whose category attribute meets a preset condition according to a set extraction rule to form a first data set, the extraction rule set according to part of the service indicators; taking and clustering the service data that are not extracted as a second data set; determining a classification result of the service data according to the first data set and a clustering result of the second data set; extracting service data of definite category attributes according to the extraction rule; and determining a classification result of service data according to a result of clustering of the service data which are not extracted and the service data having definite category attributes.

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Beijing, China

Qiang Yan