A medical device includes a QR generator and a user access/activity log. The QR generator generates a QR code at least from a username of a user and at least one OTP (one-time password) for the user and enables access of the user to the medical device upon receiving an OTP from the user. The user sends the QR code to an online authorization server for the medical device for decryption upon authentication of the user. The log lists user activity once the user is authenticated by the server. The server receives the QR code, which includes at least an encrypted text containing at least the OTP and a user identification, and decrypts the encrypted text using a private key associated with the medical device. The authorization server enables the user to log in for authentication and, if authenticated, displays the at least one OTP to the user.

Some embodiments are directed to a categorization system for categorizing a sensitive data field in a dataset, e.g., a disease classification according to the ICD classification. A client device is to obtain categories for one or more records of the dataset. The client device determines categorization data for the categorization. The categorization data comprises homomorphic encryptions of possible values of the sensitive data field and encodings of the categories associated to the respective possible values, thus keeping the categorization secret. A data provider device stores the dataset and determines homomorphic encryption indicating differences between the value of the sensitive data field for a record and respective possible values. A categorization device determines which of those encryptions indicates a match and provides a category encoding associated with a matching possible value to the client device. The client device associates the encoded category to the record.

A method and a system for managing healthcare records of a user are provided. The method includes storing an electronic medical record related to the user in form of a non-fungible token (NFT) written to a blockchain, associating a smart contract to the NFT in the blockchain, authorizing a request to access the electronic medical record related to the user based on the defined ownership of the electronic medical record stored in the blockchain, identifying one or more NFTs from the blockchain comprising one or more electronic medical records related to the user based on processing of the identifier information in associated one or more smart contracts therewith, in response to the request, and sending the one or more electronic medical records corresponding to the identified one or more NFTs to a requestor associated with the request.

A highly secure networked system and methods for storage, processing, and transmission of sensitive information are described. Sensitive, e.g. personal/private, information is cleansed, salted, and hashed by data contributor computing environments. Cleansing, salting, and hashing by multiple data contributor computing environments occurs using the same processes to ensure output hashed values are consistent across multiple sources. The hashed sensitive information is hashed a second time by a secure facility computing environment. The second hashing of the data involves a private salt inaccessible to third parties. The second hashed data is linked to previously hashed data (when possible) and assigned a unique ID. Data dictionaries are created for particular individuals provided access to the highly secure information, e.g. researchers. Prior to a data dictionary being accessible by a researcher computing device, the data dictionary undergoes compliance and statistical analyses regarding potential re-identification of the source unhashed data. The data dictionaries are viewable by researchers as certified views via a secure VPN.

Provided are methods and systems for performing a secure machine learning analysis over an instance of data. An example method includes acquiring, by a client, a homomorphic encryption scheme, and at least one machine learning model data structure. The method further includes generating, using the encryption scheme, at least one homomorphically encrypted data structure, and sending the encrypted data structure to at least one server. The method includes executing a machine learning model, by the at least one server based on the encrypted data structure to obtain an encrypted result. The method further includes sending, by the server, the encrypted result to the client where the encrypted result is decrypted. The machine learning model includes neural networks and decision trees.

Provided is a method of building a cloud-based medical image database for protecting patient information and reading medical image therefrom, the method including: acquiring a medical image of a patient by using a medical apparatus; separating medical information data and patient information data from the medical image; encrypting the patient information data by using a block chain technique; separately transmitting the encrypted patient information data and the medical information data to the cloud-based medical image database, and storing the same in the cloud-based medical image database; decrypting the encrypted patient information data stored in the cloud-based medical image database by using a block chain technique; and performing diagnosis and consulting by reading the medical information data and the patient information data of the medical image according to a big data processing algorithm. Therefore, the method may protect patient's personal information and determine the cause and progress of a disease.

Domain and/or event type-specific consensus processes for distributed ledger are provided. A consensus request is received by a core consensus engine. The consensus request corresponds to an event, the event (i) corresponds to a domain and (ii) has a type, and the consensus request comprises information corresponding to the event. Information corresponding to the event and the type are provided to the processing manager corresponding to the domain. The processing manager identifies a set of processing objects based on the type. The processing manager calls at least one processing object of the set via a corresponding interface and provides information corresponding to the event to the called processing object. The processing object is executed to generate a corresponding object result. The processing manager generates an aggregate result based on the object results. The core consensus engine determines a consensus response based at least in part on the aggregate result.

An implantable medical device (IMD) configured to communicate with an external device (ED). The ED supports two way RF communications and has a light source. The IMD includes a processor coupled to an optical detector, the processor is configured to verify that light is being received from the ED light source and that the ED is a trusted device, establishing a unidirectional optical channel from the ED to the IMD. An RF transceiver is coupled to the processor, the processor being configured permit two way RF communications with the ED only under a condition that the ED is verified as a trusted device. The processor may be configure to wake up periodically or aperiodically to check for the presence of light from the ED light source. The processor may be configured to detect a multi-bit message from the ED via the unidirectional optical channel. The multi-bit message may include a key.

An apparatus and a method capable of effectively providing services in a mobile communication system, and a data processing method of a data reception apparatus are provided. The apparatus and method includes obtaining, by a first lower packet data convergence protocol (PDCP) layer and a second lower PDCP layer, a plurality of PDCP packet data units (PDUs) based on data received from a first base station (BS) and a second BS, parallel deciphering, by the first and second lower PDCP layers, the plurality of PDCP PDUs, transmitting, from the first and second lower PDCP layers to an upper PDCP layer, the plurality of deciphered PDCP PDUs, and reordering, by the upper PDCP layer, the plurality of deciphered PDCP PDUs.

Systems and methods may use healthcare protocols for use with distributed ledger technology. The healthcare protocols may be used to, among other things, classify event data items as distributed ledger data items such that record of such classified event data items may be stored in a distributed ledger. Further, the healthcare protocols may be modified over time, and record of such modifications may also be stored in a distributed ledger.