A method for data security including receiving a first recordset, said first recordset including a first poly-identifier representing a first personally identifiable information (PII), and a first contextual information, said first poly-identifier associated with a name field of a record in a PII structured data store. Also receiving at the server a second recordset, said second recordset including a second poly-identifier representing a second personally identifiable information (PII) and a second contextual information, said second poly-identifier comprised of unique characters associated with the name field of a record in the PII structured data store. Then comparing the first and second contextual information to calculate a correlation score to create a match table entry as a result of said comparing, said match table entry including both an internal ID and an external anonymous ID. The IDs may associate the contextual information between records to a single person.

In one aspect, a digitized solution for an unbiased entity to verify and/or notarize/attest digital interactions along with interaction data between parties. The parties can be corporate entities or consumers and a between parties relationship can refer to corporate to corporate, corporate to consumer, consumer to consumer or group of corporate entities and consumers The digital interaction comprises consent agreement, data rights access, notifications/alerts, use of services and communications. A verify operation involves identifying the identity of the parties and their use of digital services. A verification of identity involves the verification by email, verification by SMS and/or verification by a Hypertext Transfer Protocol (HTTP) cookie. Notarizing or attesting involves a process of collecting interaction data. The interaction data comprises an interaction term, an interaction detail , an interaction message, a time of event, an internet protocol address, a location, a digital fingerprint. The digitized solution stores the interaction data in a centralized system where the interaction data is accessible to all relevant parties.

Described are techniques for privacy-preserving endorsements in blockchain transactions. The techniques include a method comprising associating a ledger key in a local collection with an ephemeral key, where the ephemeral key is a re-randomization of a key associated with a first organization. The method further comprises generating, by a first peer associated with the first organization, an anonymous endorsement of a transaction in a blockchain using the ephemeral key. The method further comprises determining, by a second peer associated with the first organization, that the first peer endorsed the transaction. The method further comprises retrieving, by the second peer, a preimage from the first peer. The method further comprises providing information including the anonymous endorsement and the transaction to a second organization associated with the blockchain, where the anonymous endorsement is anonymous to peers associated with the second organization.

An example computer-implemented system maintains user profiles and displays external content. Method and system are provided for performing attribution of conversions with respect to the external content in a privacy safe manner by anonymizing personally identifiable information utilizing cryptographic salt.

The disclosed systems, and methods are directed to a method for Privacy Preserving Inference (PPI) comprising receiving a first set of matrix information from a client device, generating k.sub.c−1 matrices by operating a first CSPRNG associated with the server with k.sub.c−1 seeds, computing inferences from the set of k.sub.c matrices, generating a matrix S.sub.s, generating k.sub.s−1 random matrices, computing a matrix Y.sub.k.sub.s in accordance with the inference matrix Y, the matrix S.sub.s and the k.sub.s−1 random matrices, transmitting a second set of matrix information to the client device, the second set of matrix information includes k.sub.s−1 seeds corresponding to the k.sub.s−1 random matrices and the matrix Y.sub.k.sub.s, receiving a matrix U from the client device, and computing an inference value y from the matrix U.

A protocol that is managed by a coordinating network element or third-party intermediary or peer network elements and utilizes tokens prohibits any subset of a union of the coordinating network element or third-party intermediary, if any, and a proper subset of the processors involved in token generation from substantively accessing underlying data. By one approach, processors utilize uniquely-held secrets. By one approach, an audit capability involves a plurality of processors. By one approach, the protocol enables data transference and/or corroboration. By one approach, transferred data is hosted independently of the coordinating network element. By one approach, the coordinating network element or third-party intermediary or a second requesting network element is at least partially blinded from access to tokens submitted by a first requesting network element. By one approach, a third-party intermediary uses a single- or consortium-sourced database. By one approach, network elements provisioned with tokens jointly manage the protocol.

A computer that provides one or more verified personas for a distributed token (such as a non-fungible token or NFT) of a first user is described. Notably, the computer may provide the one or more verified personas for the first user that are based at least in part on their account(s) with a provider of a secure, virtual private network (SVPN) of the first user. Consequently, the identity of the first user may be known to the provider. However, the one or more verified personas may obfuscate the known identity of the first user when conducting one or more discrete secure transactions (such as a transaction associated with a cryptocurrency or the NFT) using or associated with the distributed token. In particular, the first user may associate or link the one or more verified personas with the distributed token, thereby providing the benefits of privacy and selective (as-needed) identification.

Aspects of the disclosure relate to dynamic record masking in a blockchain system. A computing platform may receive an input dataset. The computing platform may generate a plurality of records based on data from the input dataset. The computing platform may generate one or more masked records based on the plurality of records and masking settings. The computing platform may send the records and/or masked records to destination computing platforms. The computing platform may receive notifications from the destination computing platforms indicating the records and/or masked records have been verified. The computing platform may send messages comprising instructions to the destination computing platforms to add the records or masked records to their distributed ledgers. The computing platform may send an instruction to a database platform to update one or more tables with the record.

A distributed transaction and data storage platform including a distributed notary ledger, chain arrayed data store or blockchain and one or more individual user micro-identifier chains that together enable the secure effectuation, recordation and sharing of one or more transactions including electronic health record non-fungible tokens, and/or cybersecured storage of data in an automated, real-time, zero-trust, globally data law and privacy law centric manner while maintaining transaction party confidentiality and preventing chain poisoning.

The present disclosure provides systems and methods for content quasi-personalization or anonymized content retrieval via aggregated browsing history of a large plurality of devices, such as millions or billions of devices. A sparse matrix may be constructed from the aggregated browsing history, and dimensionally reduced, reducing entropy and providing anonymity for individual devices. Relevant content may be selected via quasi-personalized clusters representing similar browsing histories, without exposing individual device details to content providers.