Abstract: Disclosed are a computer-hosted database system providing cryptographic verifiability and comprising an immutable key-value data store and cryptographic proof data (which may be stored in a Merkle Hash Tree). The logical structure of a data record is defined by a rich data model and comprises a first field and a second field. Disclosed is a method that comprises receiving an instruction requesting retrieval of first field data and returning the data stored in the first field and cryptographic proof data sufficient to prove data originality of the first field data without knowledge of the data in the second field. The rich data model may be a relational data model or a document data model.

Abstract: Cryptographically-verifiable immutable database systems and methods for permanently deleting transaction value data from the immutable database system, preserving transaction verification data. Database schema data may be preserved when transaction value data is deleted.

Abstract: Disclosed herein are computer-implemented methods of, and computer systems for, constructing dual cryptographic proofs for transactions where transaction data is stored in a cryptographically-linked data file and in an incremental hash tree, where a dual cryptographic proof includes linear cryptographic proof data from the cryptographically-linked transaction file and binary cryptographic proof data from the incremental hash tree. The transaction may include one or more key-value pairs.

Abstract: Disclosed are a computer-hosted database system providing cryptographic verifiability and comprising an immutable key-value data store and cryptographic proof data (which may be stored in a Merkle Hash Tree). The logical structure of a data record is defined by a rich data model and comprises a first field and a second field. Disclosed is a method that comprises receiving an instruction requesting retrieval of first field data and returning the data stored in the first field and cryptographic proof data sufficient to prove data originality of the first field data without knowledge of the data in the second field. The rich data model may be a relational data model or a document data model.

Abstract: Aspects create a tree data structure that indexes a collection of documents present in a data repository at a point in time. The tree data structure includes a plurality of nodes. For each such node, a respective root hash value of that node is determined. The root hash value of a leaf node is determined from hash value(s) for element(s) of that node that are keyed to documents in the collection. The root hash value of a parent node is determined from a root hash value for each of its child nodes. For a given document that is purported to be a target document present in the data repository at the point in time, processing is performed that uses the tree data structure in facilitating verification that the given document is the target document. This includes providing a cryptographic proof to demonstrate whether the given document is the target document.

Abstract: Device configuration based on predicting a health affliction. A process acquires measurements of conditions that a user is experiencing. The process predicts, based on the measurements, whether the user will experience a particular health affliction. Based on predicting that the user will experience the particular health affliction, the process configures devices of an environment in which the user is present to reduce effects of the devices on symptoms of the particular health affliction. The configuring includes adjusting a respective at least one state of each device of the devices.