Methods and systems for determining provenance and identity of digital advertising requests solicited by publishers and intermediaries representing publishers

A computer implemented method for determining provenance and using machine learning to generate a digital identity of a first entity purporting to be a second entity defined by one of a of a publisher and an intermediary representing the publisher, the computer implemented method comprising:

• identifying a plurality of metrics of a digital advertising request that is received, using a transceiver of a computing device, from a first entity computing device of the first entity, wherein the plurality of metrics comprises at least one of (i) a serving constraint having a serving constraint value and (ii) a protocol having a protocol value;
• converting the plurality of metrics to a plurality of embeddings;
• generating, with a processor using machine learning, a first portion of the digital identity associated with the first entity based on the plurality of embeddings;
• comparing, with the processor using machine learning, the first portion of the digital identity to a second digital identity associated with the second entity;
• and
• generating, with the processor using machine learning, a level of confidence that the first entity is the second entity that it purports to be.

A computer implemented method for determining provenance and using machine learning to generate a digital identity of a first entity purporting to be a second entity defined by one of a publisher and an intermediary representing the publisher, the computer implemented method comprising:

• identifying a plurality of metrics of a digital advertising request that is received, using a transceiver of a computing device, from a first entity computing device of the first entity, wherein the plurality of metrics comprises at least one of (i) a serving constraint having a serving constraint value and (ii) a protocol having a protocol value;
• converting the plurality of metrics to a plurality of embeddings;
• generating, with a processor using machine learning, a first portion of the digital identity associated with the first entity based on the plurality of embeddings;
• comparing, with the processor using machine learning, the first portion of the digital identity to a second digital identity associated with the second entity that the first entity purports to be;
• generating a response to the digital advertising request comprising a solicitation for a reply message, from the first entity, comprising at least one second metric related to at least one second digital identity metric of the second digital identity that is not identified in the digital advertising request;
• generating, with the processor using machine learning, a second portion of the digital identity of the first entity based on the reply message;
• and
• generating, with the processor using machine learning, the digital identity comprising the first portion and the second portion of the digital identity.

A computer implemented method for determining provenance and using machine learning to generate a digital identity of a first entity purporting to be a second entity defined by one of a publisher and an intermediary representing the publisher, the computer implemented method comprising:

• identifying a plurality of metrics of a digital advertising request that is received, using a transceiver of a computing device, from a first entity computing device of the first entity;
• processing the plurality of metrics using at least one clustering algorithm to identify a second entity metric of a second entity digital identity associated with the second entity that is not identified in the plurality of metrics of the digital advertising request;
• and
• generating, using the at least one clustering algorithm, a level of confidence that the first entity is the second entity.