A method for ensuring precedence for the processing of a blockchain transaction to prevent loss of cryptographic currency includes receiving a new blockchain transaction by a blockchain node in a blockchain network, confirming the new blockchain transaction including identifying a precedence transaction that was previously conducted and stored in the blockchain that involves both blockchain wallets included in the new blockchain transaction, including the new blockchain transaction in a new block that is generated, and distributing the new block to additional nodes in the blockchain network for confirmation and addition to the blockchain.

Apparatus and associated methods relate to a singular event notification system (SENS). In an illustrative example, the SENS may automatically authenticate a singular event signal and dynamically generate an event action package as a function of an event notification signal. The operation, for example, may include retrieving a user notification profile (UNP) associated with a user account upon receiving an event notification signal (ENS). For example, the UNP may include relevant entities to be notified in response to the ENS. If the ENS is authorized based on the UNP, for example, the SENS apply an action model to the UNP to generate an event action package to be transmitted to a predetermined destination. For example, the event action package may include customized forms and contact instructions generated based on entities identified based on the UNP. Various embodiments may advantageously generate a timely and secure response to the relevant entities.

Embodiments of the present disclosure provide hierarchical, differential privacy enhancements to federated, machine learning. Local machine learning models may be generated and/or trained by data owners participating in the federated learning framework based on their respective data sets. Noise corresponding to and satisfying a first privacy loss requirement are introduced to the data owners' respective data sets, and noise corresponding to and satisfying a first privacy loss requirement are introduced to the local models generated and/or trained by the data owners. The data owners transmit model data corresponding to their respective local models to a coordinator, which in turn aggregates the data owners' model data. After introducing noise corresponding to and satisfying a third privacy loss requirement to the aggregated model data, the coordinator transmits the aggregated model data to the data owners to facilitate updating and/or re-training on their respective machine learning models.

There is a need for more effective and efficient secure data transmission. This need can be addressed by, for example, solutions for secure data transmission that utilize per-user-functionality secret shares. In one example, a method includes generating a hashed user identifier based on a received user identifier; transmitting the hashed user identifier to an external computing entity; and receiving a data retrieval secret share from the external computing entity, wherein: (i) the data retrieval secret share is selected from a plurality of per-user-functionality secret shares, (ii) the plurality of per-user-functionality secret shares are generated based on a secret value, (iii) the secret value is generated based on the hashed user identifier, (iv) the secret value is used to generate a user data private key, and (v) the external computing entity is configured to encrypt user-provided data using the user data private key prior to transmission of the encrypted user-provided data.

Precomputed and transactional mixing is believed to allow portable devices, such as smart phones, to send and receive messages, with little extra bandwidth or battery usage, while achieving anonymity for senders and recipients among all messages sent globally in batches defined by short time intervals. To learn anything about which inputs correspond with which outputs of such a batch of messages, the entire cascade of mix devices, each preferably operating independently in a different country, would it is believed have to be compromised,

None of the real-time computation, neither by the mixes nor smartphones, uses full public-key operationsresulting it is believed in orders of magnitude performance improvement over previously-known systems.

Aspects include untraceable return addresses, group chat, feed-following and large payloads. Transaction protocols include a variety of payments use cases. Limited anonymity and credential mechanism are based on a new approach to user identification disclosed, in which each user provides a small amount of different identifying information to each mix node, so that comparatively little is revealed to each node individually.

Provided is an electronic device, including a database and a processor, and the processor is configured to receive a user ID, at least one anonymous user ID corresponding to the user ID and location information of a user terminal from the user terminal, receive an unmanned aerial vehicle (UAV) ID, at least one anonymous UAV ID corresponding to the UAV ID and location information of an UAV from the UAV, and in response to receiving key exchange request information from the user terminal or the UAV, provide a key exchange protocol between the user terminal and the UAV based on the location information of the user terminal and the location information of the UAV.

A method for confidentially querying the presence of a record in a database hosted by a server, the records being stored in the database in the form of digital footprints obtained by hashing a record by a public hash function. The footprints are masked by a stream cipher using a symmetric key of a first user. The first user may grant a second user authorisation to query the database by transmitting the inverse masks of various rows, encrypted by the public key of an additive homomorphic cryptosystem of the second user. The rows of the database are unmasked in the homomorphic domain and the second user transmits an encrypted request to query the base according to a PIR protocol. The second user can decrypt the response from the server using the private key of their homomorphic cryptosystem and determine whether the footprint sought is present in the response thus decrypted.

A computer platform includes an artificial neural network (ANN) as well as a classifier. The ANN is configured, after a learning phase, to transform an input data vector into a discriminating feature vector having a smaller dimension. A user then generates, from a plurality of reference data vectors, the same plurality of reference feature vectors, which are encrypted in an encryption module using the public key of a homomorphic cryptosystem and stored in a reference database of the platform. When the user requests the classification of an input data vector, the ANN, or a copy thereof, provides the classifier with a corresponding discriminating feature vector (y). Distances from the vector to the different reference feature vectors are calculated in the homomorphic domain and the index of the reference feature vector closest to y, i.e. the identifier i.sub.0 of the class to which it belongs, is returned to the user.

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 present invention addresses the issue of secure and trusted Internet of Things (IoT) blockchain networks by adopting the emerging blockchain technologies. The present invention proposes a new hybrid blockchain technology to address the trusted IoT issues such as trustless communications and decentralized applications. Besides, the present invention also disclose that the pseudonymous authentication technique can use a puzzle-solving computation to enable trustless communications for the IoT and provide the capabilities of near real-time transactions.