Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable storage media for implementing a scalable, secure, efficient, and adaptable distributed digital ledger transaction network. Indeed, the disclosed systems can reduce storage and processing requirements, improve security of implementing computing devices and underlying digital assets, accommodate a wide variety of different digital programs (or “smart contracts”), and scale to accommodate billions of users and associated digital transactions. For example, the disclosed systems can utilize a host of features that improve storage, account/address management, digital transaction execution, consensus, and synchronization processes. The disclosed systems can also utilize a new programming language that improves efficiency and security of the distributed digital ledger transaction network.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable storage media for implementing a scalable, secure, efficient, and adaptable distributed digital ledger transaction network. Indeed, the disclosed systems can reduce storage and processing requirements, improve security of implementing computing devices and underlying digital assets, accommodate a wide variety of different digital programs (or “smart contracts”), and scale to accommodate billions of users and associated digital transactions. For example, the disclosed systems can utilize a host of features that improve storage, account/address management, digital transaction execution, consensus, and synchronization processes. The disclosed systems can also utilize a new programming language that improves efficiency and security of the distributed digital ledger transaction network.

Abstract: A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

Abstract: Sharing of user preferences is described. In an embodiment a user preference associated with a user is shared with a group of users in order to improve the relevance of results they receive. A database is used to store information detailing a number of groups of users extracted from a social network graph, where the social network graph describes connections between users. On receipt of a user preference associated with a user, a group of users containing the user is selected and the user preference is then shared with everyone in the selected group. In a further embodiment, the groups of users in the database may comprise cohesive groups of users and an extended group associated with each cohesive group. When selecting a group to share preference data with, a cohesive group containing the user is first selected and then the preference data is shared with the corresponding extended group.

Abstract: A protocol to provide security, privacy and robustness in the context of distributed metering statistics is described herein. Blinded readings may be produced by a subset of meters. A service provider receives a subset of blinded readings and calculates to uncover their weighted sum. To achieve this, a de-blinding value may also be requested by the service provider from a set of aggregation authorities.

Abstract: Privacy-preserving metering with low overhead is described. In an embodiment consumption of a resource such as electricity, car insurance, cloud computing resources is monitored by a meter and bills are created in a manner which preserves privacy of a customer but at the same reduces bandwidth use between a meter and a provider of the resource. For example, fine grained meter readings which describe customer behavior are kept confidential without needing to send large cryptographic commitments to meter readings from a meter to a provider. In an example, meter readings are encrypted and sent from a meter to a provider who is unable to decrypt the readings. In examples a cryptographic signature is generated to commitments to the meter readings and only the signature is sent to a provider thus reducing bandwidth. For example, a customer device is able to regenerate the commitments using the signature.

Abstract: Privacy-preserving metering with low overhead is described. In an embodiment consumption of a resource such as electricity, car insurance, cloud computing resources is monitored by a meter and bills are created in a manner which preserves privacy of a customer but at the same reduces bandwidth use between a meter and a provider of the resource. For example, fine grained meter readings which describe customer behavior are kept confidential without needing to send large cryptographic commitments to meter readings from a meter to a provider. In an example, meter readings are encrypted and sent from a meter to a provider who is unable to decrypt the readings. In examples a cryptographic signature is generated to commitments to the meter readings and only the signature is sent to a provider thus reducing bandwidth. For example, a customer device is able to regenerate the commitments using the signature.

Abstract: Privacy protecting metering is described such as for electricity, gas or water metering, metering use of cloud computing resources, traffic congestion charging and other metering applications. In examples, fine grained user consumption data is kept private and not disclosed to a provider of a resource consumed by the user. In examples, a bill generator receives certified meter readings and a certified pricing policy and generates a bill which omits fine grained user consumption data. For example, the bill generator generates a zero knowledge proof that the bill is correct and sends that proof to a provider together with the bill. In examples a provider is able to check that the bill is correct using the zero knowledge proof without finding out the user's private consumption data. In an embodiment the pricing policy is stored as signed rows of a table to enable efficient generation of the zero knowledge proof.

Abstract: Sharing of user preferences is described. In an embodiment a user preference associated with a user is shared with a group of users in order to improve the relevance of results they receive. A database is used to store information detailing a number of groups of users extracted from a social network graph, where the social network graph describes connections between users. On receipt of a user preference associated with a user, a group of users containing the user is selected and the user preference is then shared with everyone in the selected group. In a further embodiment, the groups of users in the database may comprise cohesive groups of users and an extended group associated with each cohesive group. When selecting a group to share preference data with, a cohesive group containing the user is first selected and then the preference data is shared with the corresponding extended group.