Abstract: Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

Abstract: Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

Abstract: Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

Abstract: Systems and methods are disclosed herein for improved per-frequency counting systems that record interactions between individuals and a group of providers while maintaining differential privacy. A protocol may be defined that specifies frequency bins to categorize identifiers corresponding to individuals. A provider may generate a plurality of private sketches, each corresponding to a plurality of frequencies defined in the protocol. Frequency data is determined for each identifier. Identifiers are encoded into the private sketches corresponding to the identifiers’ associated frequency. The plurality of private sketches from each provider in the group of providers are combined to generate a deduplicated distribution across the group. In one implementation, the private sketches of each provider are sequentially merged until all sketches have been combined, from which the total distribution can be estimated.

Abstract: Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

Abstract: Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

Abstract: Systems and methods for generating min-increment counting bloom filters to determine count and frequency of device identifiers and attributes in a networking environment are disclosed. The system can maintain a set of data records including device identifiers and attributes associated with device in a network. The system can generate a vector comprising coordinates corresponding to counter registers. The system can identify hash functions to update a counting bloom filter. The system can hash the data records to extract index values pointing to a set of counter registers. The system can increment the positions in the min-increment counting bloom filter corresponding to the minimum values of the counter registers. The system can obtain an aggregated public key comprising a public key. The system can encrypt the counter registers using the aggregated shared key to generate an encrypted vector. The system can transmit the encrypted vector to a networked worker computing device.

Abstract: The present disclosure provides systems and methods for entropy balanced population measurement. Entropy balancing is a statistical technique for preprocessing data to achieve covariate balance. Weighting coefficients may be dynamically adjusted to satisfy balance conditions or constraints to adjust for inequalities in representation, thereby improving covariate moments. Using entropy balancing and linear regression analysis with panel content exposure and results data provides a mechanism to estimate the effects of multiple exposure vectors simultaneously, including instances where panelists are exposed to a vector multiple times. Data may be obfuscated or anonymized for preprocessing via a double-encrypted intersection-based extraction mechanism, allowing both measurement systems and panel providers to retain confidential information.

Abstract: Methods and systems of reducing computer resource utilization to update rankings based on dynamically generated content are described. The method includes identifying a ranking of content experience types of a list of content experience types. Each content experience type including parameters to generate content experience documents. The method also includes updating the ranking of content experience types. The updating includes iteratively selecting content experience types from the ranking of content experience types to generate an update subset of the ranking of content experience types, generating, for a client device, a feedback request document including a plurality of content experience documents, and receiving feedback information related to the plurality of content experience documents included in the feedback request document to update the ranking of the content experience types included in the update subset.

Abstract: A method, executed by a processor, for estimating media metrics from large population data includes formatting and storing panel data, the panel data comprising observed viewing data of a plurality of individual panelists and demographic data for the plurality of panelists, the panel being drawn from a large population; accessing the large population data, the large population data comprising household-level viewing data and household level demographics; training a model to estimate viewing audience size based on the observed panel data; estimating, using the trained model, audience size for each household in the large population data; estimating a viewing score for each individual viewer in a plurality of households in the large population data; and combining the estimates of audience size and viewing score to produce probabilities that each of the viewers in the household viewed a specific media event.

Abstract: Systems and techniques are provided for optimizing personalized recommendations with longitudinal data and a future objective. An identifier may be received for content items. A user content item history including a list identifying a previously acquired content item may be received. Content item metadata may be received including a correlation between the previously acquired content item and a content item for which an identifier was received, and a correlation between a content item for which an identifier was received and fulfillment of a future objective. A joint probability may be determined for each content item based on the user content item history and the content item metadata, including the probability that the content item will be acquired by the user after being recommended to the user and that a future objective will be fulfilled after the content item is acquired by the user.