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, such as a web browser, includes a pipeline optimized for faster, more secure, and more private, viewing of hypermedia documents using a reader mode. The reader mode is “always on” in the sense that a classifier runs on every web page and every compatible page is rendered in the reader mode and not rendered in full, referred to as the bloat page. Significant time savings are gained by avoiding fetching and rendering the bloat page at all because the bloat page devours network bandwidth and computing resources. Avoiding loading the bloat page also avoids exposing the user to what are often abusive privacy infringements and security vulnerabilities from running executable code in the browser, while providing an uncluttered viewing experience of content that is actually of interest to the user.

Abstract: A decentralized and trust-minimizing computer architecture for computing rewards for users of an advertising system includes cryptographic black box accumulators (BBA), which is a cryptographic counter that only the issuer can update. An attention application requests initialization of a BBA from a guardian and subsequently requests updates to the BBA to track interactions between a user of the attention application and ads on the attention application. The guardian signs updates to the BBA to reach agreement on the state of ad interactions. The attention application may randomize the BBA and submit requests via an anonymous channel such that no participant can link two encounters with the BBA to each other or link the BBA to a specific attention application, thus improving user privacy. Reward redemption requests can be made based on a known policy and committed to a public blockchain for verification by observers that the protocol is operating correctly.

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, such as a web browser, includes a pipeline optimized for faster, more secure, and more private, viewing of hypermedia documents using a reader mode. The reader mode is “always on” in the sense that a classifier runs on every web page and every compatible page is rendered in the reader mode and not rendered in full, referred to as the bloat page. Significant time savings are gained by avoiding fetching and rendering the bloat page at all because the bloat page devours network bandwidth and computing resources. Avoiding loading the bloat page also avoids exposing the user to what are often abusive privacy infringements and security vulnerabilities from running executable code in the browser, while providing an uncluttered viewing experience of content that is actually of interest to the user.