Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: An attention application measures a user's attention focused on publisher content and advertisements to create an attention metric. Attention can be measured via hardware sensors or by user interactions with input/output hardware. A user attention metric profile can be used to modify content, content presentation, and/or match ads. Aggregate attention metrics can be used by publishers or third parties. Attention consumers may reward attention with a digital asset. A proof-of-attention can be made based on secure attention sensor hardware and/or a zero-knowledge proof.

Abstract: An attention application measures a user's attention focused on publisher content and advertisements to create an attention metric. Attention can be measured via hardware sensors or by user interactions with input/output hardware. A user attention metric profile can be used to modify content, content presentation, and/or match ads. Aggregate attention metrics can be used by publishers or third parties. Attention consumers may reward attention with a digital asset. A proof-of-attention can be made based on secure attention sensor hardware and/or a zero-knowledge proof.

Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.