Abstract: 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.

Abstract: Implementations of this specification provide query optimization methods, apparatuses, and systems for secure multi-party databases. In an implementation, a method includes: receiving a current query associated with a plurality of target database of a multi-party database system, generating a plurality of execution plans for the current query, determining, for each execution plan, a respective cost computation formula of a plurality of cost computation values for computing an execution cost of jointly executing the execution plan by the plurality of target databases, receiving a secure computation result from each of a plurality of query engines corresponding to the plurality of target databases, and determining an optimal execution plan having a lowest cost value in the plurality of cost computation formulas based on the secure computation result.

Abstract: A power transmission and transformation project acceptance method based on building information modeling (BIM) and point cloud measurement includes: S1: constructing a BIM model based on a design drawing, where the BIM model includes standard parameters of key components of a power transmission and transformation project; S2: obtaining point cloud data, and constructing a point cloud data model based on the point cloud data, where the point cloud data model includes actual parameters of the key components of the power transmission and transformation project; S3: comparing the BIM model with the point cloud data model to obtain construction acceptance data; and S4: visualizing the construction acceptance data and outputting an acceptance report to complete acceptance of the power transmission and transformation project. The BIM model and the point cloud data model are compared to implement rapid and efficient acceptance of the power transmission and transformation project.

Abstract: 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.

Abstract: Methods and systems for evaluating privacy protection are provided. A method includes determining a first sub-dataset of a first dataset based on a sampling rate, a tolerance, and a first threshold. The method also includes determining a second sub-dataset of the first dataset based on the sampling rate, the tolerance, and a second threshold. The method includes determining a first distance between the first sub-dataset and the first threshold, determining a second distance between the second sub-dataset and the second threshold, generating a first intersection of the first sub-dataset and a second dataset and updating posterior for elements of the first dataset based on the first intersection when the first distance is less than or equal to the second distance, and determining positive membership and negative membership for the elements of the first dataset in the second dataset based on the posterior for the elements of the first dataset.

Abstract: A vehicle-type driving device includes a traveling assembly including multiple traveling wheels; a steering wheel configured to control the vehicle-type driving device to steer; a traveling motor for driving the vehicle-type driving device to steer at a target angular velocity, where the ratio of the target angular velocity to the change in the steering angle of the steering wheel is defined as a steering ratio; and a control device configured to, when the traveling speed of the vehicle-type driving device is within a preset range, set the steering ratio according to a steering ratio curve function, where variables of the steering ratio curve function include the steering angle of the steering wheel and the traveling speed of the vehicle-type driving device.

Abstract: Methods and systems for evaluating privacy protection are provided. A method includes generating a first intersection of a first dataset and a second dataset. The method also includes determining a partition number based on the first dataset, the first intersection, and a number of iterations by: determining a privacy leakage for subsets of the first dataset based on the first dataset, the first intersection, and the number of iterations; and determining the partition number based on the privacy leakage for each of the subsets of the first dataset. The method further includes partitioning the first dataset into a first sub-dataset and a second sub-dataset using the partition number, and determining positive membership and negative membership for elements of the first sub-dataset in the second dataset and for elements of the second sub-dataset in the second dataset.

Abstract: Embodiments of this specification provide a method for jointly training a natural language processing (NLP) model based on privacy protection. The NLP model includes an encoding network located at a first party and a processing network located at a second party. According to the method, the first party obtains a local target training statement, inputs the target training statement to the encoding network, and forms a sentence representation vector based on an encoding output of the encoding network; and then adds target noise that conforms to differential privacy to the sentence representation vector, to obtain a target noise addition representation, and sends the target noise addition representation to the second party for training of the processing network.

Abstract: This specification provides a multi-party database including a central node and a plurality of databases. The central node has a disclosed first interface. Each of a plurality of query engines corresponding to the plurality of databases includes a second interface configured to interact with the first interface. The central node can determine a plurality of target databases related to a query request from the plurality of databases based on the query request; and send a query indication to a plurality of target query engines corresponding to the plurality of target databases through the first interface. The plurality of target query engines can receive the query indication from second interfaces in the plurality of target query engines, and execute the query indication to obtain a query result; and send the query result to the first interface in the central node through the second interfaces.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments of this specification provide a differential privacy-based feature processing method and apparatus. The method relates to a first party and a second party, the first party stores a first feature portion of a plurality of samples, the second party stores a plurality of binary classification labels corresponding to the plurality of samples, and the method includes: The second party separately encrypts the plurality of binary classification labels corresponding to the plurality of samples, to obtain a plurality of encrypted labels. The first party determines, based on the plurality of encrypted labels and a differential privacy noise, a positive sample encrypted noise addition quantity and a negative sample encrypted noise addition quantity corresponding to each bin in a plurality of bins. The plurality of bins are obtained by performing binning processing on the plurality of samples for a feature in the first feature portion.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for data communication with differentially private intersection dataset. On example method includes: determining a plurality of bias parameters based on a parameter for establishing a differential privacy (DP) data protection; determining an intersection dataset based on client data from a client device and server data from the server device; updating the intersection dataset based on the client data and the plurality of bias parameters; and returning the updated intersection data having the DP data protection to the client device.

Abstract: Implementations of the present specification provide a query processing method and apparatus. The method includes: first, determining query types of L queries to be performed on a target dataset, where the target dataset includes data of an object; next, determining query sensitivity of each query type of the query types for the target dataset; and then, determining, based on the query sensitivity corresponding to each query and a privacy budget parameter predetermined for a total set of the L queries, a noise power allocated to each query. Based on this, for a target query in the L queries, an actually returned result of the target query can be determined as an original query result of the target query added with a target noise sampled from target noise distribution of differential privacy, where the target noise distribution is determined based on the noise power allocated to the target query. As such, privacy of the target dataset can be protected.

Abstract: The invention relates to a method for connecting a connection piece to a U-shaped ring anchor for a head module for rail vehicles, the head module consisting predominantly of fiber-reinforced plastic material and having an outer shell and the ring anchor consisting of fiber-reinforced plastic material and being arranged in the roof region of the outer shell as a stiffening means. According to the method, connecting elements are fed through the dry or matrix-material-impregnated, unconsolidated fiber reinforcement structure of the ring anchor.

Abstract: Implementations of this specification provide query optimization methods, apparatuses, and systems for secure multi-party databases. In an implementation, a method includes: receiving a current query associated with a plurality of target database of a multi-party database system, generating a plurality of execution plans for the current query, determining, for each execution plan, a respective cost computation formula of a plurality of cost computation values for computing an execution cost of jointly executing the execution plan by the plurality of target databases, receiving a secure computation result from each of a plurality of query engines corresponding to the plurality of target databases, and determining an optimal execution plan having a lowest cost value in the plurality of cost computation formulas based on the secure computation result.

Abstract: This specification provides example data query methods, apparatuses, and systems for a multi-party secure database. In an example computer-implemented method, a central node receives a data query request from a data requester, where the data query request relates to one or more pieces of target data stored at one or more data providers. The central node determines whether the data query request satisfies respective security requirements of the one or more pieces of target data. In response to determining that the data query request satisfies respective security requirements of the one or more pieces of target data, the central node processes the one or more pieces of target data to obtain result data; and sends the result data to the data requester. The central node can restrict query of the target data by the data requester based on the security requirement of the target data stored at the data provider.

Abstract: 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.