Abstract: This disclosure relates to a privacy preserving machine learning platform. In one aspect, a method includes receiving, from a client device and by a computing system of multiple multi-party computation (MPC) systems, a first request for user group identifiers that identify user groups to which to add a user. The first request includes a model identifier for a centroid model, first user profile data for a user profile of the user, and a threshold distance. For each user group in a set of user groups corresponding to the model identifier, a centroid for the user group that is determined using a centroid model corresponding to the model identifier is identified. The computing system determines a user group result based at least on the first user profile data, the centroids, and the threshold distance. The user group result is indicative of user group(s) to which to add the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting errors in a client device and its associated applications while preserving the privacy of the user of the device. Methods can include obtaining and blinding contextual application data for an application on a device. Data regarding the application's digital certificate and device trustworthiness data are obtained and provided to a trust assessment server along with the blinded data. This server can provide indications that the device is trustworthy and the application is authentic, and can digitally sign the blinded data. The digital signature can be validated and the unblinded contextual application data can be obtained. If the unblinded data matches the contextual application data, the application can provide the digital signature, the indications, and the unblinded contextual application data to an error detection server, which in turn can indicate the application does not have errors.

Abstract: This disclosure relates to preserving the privacy of users and preventing access to information of other entities. In one aspect, a method includes receiving, from a client device, a content request including request signals specifying user group identifiers that each identify a user group that includes a user of the client device. One or more user group identifiers that satisfy a first k-anonymity process are identified. Selection parameter elements that each include data indicating a respective digital component and a selection parameter for the respective digital component are received from one or more first content platforms. At least a portion of the selection parameters and, for each selection parameter, data identifying the first content platform from which the selection parameter was received are transmitted to a second content platform. Data specifying a given first content platform selected based on the selection parameters is received from the second content platform.

Abstract: Methods, systems, and apparatus, including a method for updating user consent in a verifiable manner. In some aspects, a method includes receiving, from a client device, a request including an attestation token. The attestation token includes a set of data that includes at least a user identifier that uniquely identifies a user of the client device, a token creation time that indicates a time at which the attestation token was created, user consent data specifying whether one or more entities that receive the attestation token are eligible to use data of the user, an action to be performed in response to the request. The attestation token also includes a digital signature of at least a portion of the set of data, including at least the user identifier and the token creation time. An integrity of the request is verified using the attestation token.

Abstract: Methods, systems, and apparatus, including an apparatus for verifying the integrity of requests. In some aspects, a method includes receiving, from an application, a request including an attestation token of the application. The attestation token includes a set of data that includes at least a public key of the application and a token creation time that indicates a time at which the attestation token was created. The attestation also includes a signature of the set of data. The signature is generated using a private key that corresponds to the public key. The integrity of the request is verified using the attestation token. The verification includes determining that the integrity of the request is valid based on a determination that the token creation time is within a threshold duration of the time at which the request was received and a determination that the set of data has not been.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting errors in a client device and its associated applications while preserving the privacy of the user of the device. Methods can include obtaining and blinding contextual application data for an application on a device. Data regarding the application's digital certificate and device trustworthiness data are obtained and provided to a trust assessment server along with the blinded data. This server can provide indications that the device is trustworthy and the application is authentic, and can digitally sign the blinded data. The digital signature can be validated and the unblinded contextual application data can be obtained. If the unblinded data matches the contextual application data, the application can provide the digital signature, the indications, and the unblinded contextual application data to an error detection server, which in turn can indicate the application does not have errors.

Abstract: Methods, systems, and apparatus, including a method for determining network measurements. In some aspects, a method includes receiving, by a first aggregation server and from each of multiple client devices, encrypted impression data. A second aggregation server receives, from each of at least a portion of the multiple client devices, encrypted conversion data. The first aggregation server and the second aggregation server perform a multi-party computation process to decrypt the encrypted impression data and the encrypted conversion data. Each portion of decrypted impression data and each portion of decrypted conversion data is sent to a respective reporting system.

Abstract: This disclosure relates to using trust tokens to verify the integrity of devices and applications from which data is received. In one aspects, a method includes receiving, from a client device, a request for one or more trust tokens. The request includes at least one of one or more device-level fraud detection signals obtained from the client device or data representing code of an application that initiated the request. The request also includes a respective nonce for each of the one or more trust tokens. A determination is made, based on at least one of the one or more device-level fraud signals or the data representing the code of the application, to issue the one or more trust tokens to the client device. Each trust token is generated using the nonce for the trust token. The one or more trust tokens are provided to the client device.

Abstract: This document relates to using secure MPC to select digital components in ways that preserve user privacy and protects the security of data of each party that is involved in the selection process. In one aspect, a method includes performing, by a first server of a secure MPC system in collaboration with one or more second servers of the secure MPC system, a selection process to select a digital component based in part on a selection value for each digital component in the selection process. This includes determining a first secret share of a winner parameter for each digital component in the selection process. The first server determines, for each given digital component in the selection process and in collaboration with the second server(s), a highest other selection value that corresponds to a different digital component that is different from the given digital component.

Abstract: Methods, systems, and apparatus, including a method for preventing fraud. In some aspects, a method includes: receiving, from multiple client devices, a measurement data element that includes a respective group member key and a group identifier for a given conversion as a result of displaying a digital component. Each client device uses a threshold encryption scheme to generate, based at least on network data that includes one or more of impression data or conversion data for the conversion, a group key that defines a secret for encrypting the network data and generate, based on data related to the application, the respective group member key that includes a respective share of the secret. In response to determining that at least the threshold number of measurement data elements having the same group identifier have been received, the network data is decrypted using the group member keys in the received measurement data elements.

Abstract: Methods, systems, and apparatus, including a method for determining network measurements. In some aspects, a method includes receiving, by a first aggregation server and from each of multiple client devices, encrypted impression data. A second aggregation server receives, from each of at least a portion of the multiple client devices, encrypted conversion data. The first aggregation server and the second aggregation server perform a multi-party computation process to generate chronological sequences of encrypted impression data and encrypted conversion data and to decrypt the encrypted impression data and the encrypted conversion data.

Abstract: This disclosure relates to a privacy preserving machine learning platform. In one aspect, a method includes receiving, from a client device and by a computing system of multiple multi-party computation (MPC) systems, a first request for user group identifiers that identify user groups to which to add a user. The first request includes a model identifier for a centroid model, first user profile data for a user profile of the user, and a threshold distance. For each user group in a set of user groups corresponding to the model identifier, a centroid for the user group that is determined using a centroid model corresponding to the model identifier is identified. The computing system determines a user group result based at least on the first user profile data, the centroids, and the threshold distance. The user group result is indicative of user group(s) to which to add the user.

Abstract: This disclosure relates to preserving the privacy of users and preventing access to information of other entities. In one aspect, a method includes receiving, from a client device, a content request including request signals specifying user group identifiers that each identify a user group that includes a user of the client device. One or more user group identifiers that satisfy a first k-anonymity process are identified. Selection parameter elements that each include data indicating a respective digital component and a selection parameter for the respective digital component are received from one or more first content platforms. At least a portion of the selection parameters and, for each selection parameter, data identifying the first content platform from which the selection parameter was received are transmitted to a second content platform. Data specifying a given first content platform selected based on the selection parameters is received from the second content platform.

Abstract: The present disclosure provides systems and methods for secure identification retrieval. The method includes retrieving a value of a periodic variable and calculating a plurality of query tokens from a corresponding plurality of client device identifiers and the value of the periodic variable. Each query token is associated with a corresponding client device identifier in a first database. The method further includes receiving a first query token calculated from a client device identifier of the first client device and the value of the periodic variable and identifying a second query token of the calculated plurality of query tokens in the first database matching the first query token. The method further includes, responsive to the identification, retrieving the associated client device identifier and retrieving one or more characteristics of the first client device according to the associated client device identifier. The method further includes transmitting the retrieved one or more characteristics.

Abstract: Methods, systems, and apparatus, including a method for updating user consent in a verifiable manner. In some aspects, a method includes receiving, from a client device, a request including an attestation token. The attestation token includes a set of data that includes at least a user identifier that uniquely identifies a user of the client device, a token creation time that indicates a time at which the attestation token was created, user consent data specifying whether one or more entities that receive the attestation token are eligible to use data of the user, an action to be performed in response to the request. The attestation token also includes a digital signature of at least a portion of the set of data, including at least the user identifier and the token creation time. An integrity of the request is verified using the attestation token.

Abstract: The present disclosure provides systems and methods for secure identification retrieval. The method includes retrieving a value of a periodic variable and calculating a plurality of query tokens from a corresponding plurality of client device identifiers and the value of the periodic variable. Each query token is associated with a corresponding client device identifier in a first database. The method further includes receiving a first query token calculated from a client device identifier of the first client device and the value of the periodic variable and identifying a second query token of the calculated plurality of query tokens in the first database matching the first query token. The method further includes, responsive to the identification, retrieving the associated client device identifier and retrieving one or more characteristics of the first client device according to the associated client device identifier. The method further includes transmitting the retrieved one or more characteristics.

Abstract: Methods, systems, and apparatus, including a method for determining network measurements. In some aspects, a method includes receiving, by a first aggregation server and from each of multiple client devices, encrypted impression data. A second aggregation server receives, from each of at least a portion of the multiple client devices, encrypted conversion data. The first aggregation server and the second aggregation server perform a multi-party computation process to generate chronological sequences of encrypted impression data and encrypted conversion data and to decrypt the encrypted impression data and the encrypted conversion data.

Abstract: Methods, systems, and apparatus, including a method for preventing fraud. In some aspects, a method includes: receiving, from multiple client devices, a measurement data element that includes a respective group member key and a group identifier for a given conversion as a result of displaying a digital component. Each client device uses a threshold encryption scheme to generate, based at least on network data that includes one or more of impression data or conversion data for the conversion, a group key that defines a secret for encrypting the network data and generate, based on data related to the application, the respective group member key that includes a respective share of the secret. In response to determining that at least the threshold number of measurement data elements having the same group identifier have been received, the network data is decrypted using the group member keys in the received measurement data elements.

Abstract: This disclosure relates to a privacy preserving machine learning platform. In one aspect, a method includes receiving, by a first computing system of multiple multi-party computation (MPC) systems, an inference request that includes a first share of a given user profile. A number k of nearest neighbor user profiles that are considered most similar to the given user profile are identified. The first computing system identifies a first set of nearest neighbor profiles based on the first share of the given user profile and a k-nearest neighbor model. The first computing system receives, from each of one or more second computing systems of the multiple MPC systems, data indicating a respective second set of nearest neighbor profiles identified by the second computing system based on a respective second share of the given user profile and a respective second k-nearest neighbor model trained by the second computing system.

Abstract: This document relates to using secure MPC to select digital components in ways that preserve user privacy and protects the security of data of each party that is involved in the selection process. In one aspect, a method includes receiving, by a first server of a secure MPC system from a client device, a digital component request. The first server identifies, for each digital component in a set, a selection value and a priority tier. For each tier, the first server determines, using a secure MPC process in collaboration with one or more second servers of the secure MPC system, a first secret share of a winner parameter for each digital component in the priority tier. The first server identifies a highest tier for which a given digital component has a winner parameter that indicates that the given digital component is a winning digital component.