Fujitsu Corporation

Arnab Roy

Avradip Mandal

Hart Montgomery

John C Mitchell

Takeshi Shimoyama

Hayato Ushijima-Mwesigwa

Sarvagya Upadhyay

Hiroshi Tsuda

Yoshinori Yaginuma

Kouhei Shigeta

Ryota Kamakari

Toshiyuki Ishiguro

Indradeep Ghosh

Daisuke Mashima

Toshiya Shimizu

Jumpei Yamaguchi

Hayato Ushijima

Osman Asif Malik

Ryuichi Ohori

Jesse Hartloff

Xiaoyuan Liu

Nilanjan Datta

A method of implementing a revocable threshold hierarchical identity-based signature scheme may include receiving an identifier associated with a user. A first secret key based on the identifier may be generated. A string and the identifier may be directed to be posted on a block of a blockchain. A second secret key may be generated using the string, the first secret key, and the identifier. The block that includes the string and the identifier may be signed using the second secret key. A message may be signed using the second secret key to generate a signature. The signature may be provided to a device. The signature may be verifiable by the device using the string and the identifier obtained from the block by the device.

Cross-blockchain identity and key management

A method of facilitating an anonymous message board may include receiving a secret key share associated with a published public key. An initial table state may be generated by encrypting, via the public key, an initial table including a table index and table initial values. A user post encrypted via the public key may be received, the user post including a message and a message index value. The initial table state may be updated to an updated table state by replacing an initial table value of the initial table values with the message. In response to a time interval associated with a predetermined length of time expiring after generating the initial table state, the updated table state may be partially decrypted via the first secret key share as a partially decrypted table. The partially decrypted table may be broadcast.

Anonymous message board

According to an aspect of an embodiment, operations include receiving a Quadratic Integer Programming (QIP) problem including an objective function and a set of constraints on integer variables associated with the objective function. The operations further include obtaining an approximation of the QIP problem by relaxing the QIP problem and generating an approximate solution by solving the obtained approximation. The operations further include generating a Quadratic Unconstrained Binary Optimization (QUBO) formulation of the QIP problem based on the generated approximate solution and the received QIP problem. The operations further include submitting the generated QUBO formulation to an optimization solver machine and receiving a solution of the submitted QUBO formulation from the optimization solver machine. The operations further include publishing an integral solution of the received QIP problem on a user device based on the received solution.

Solving quadratic integer programming (QIP) problems on optimization solver machines

A method of solving a large scale combinatorial optimization problem including inputting, via at least one processor, an objective function and an initial solution as a mapping from a plurality of n nodes, randomly clustering the plurality of nodes into k clusters of n/k nodes each, for each cluster of the k clusters, assigning binary variables to denote each possible permutation of a label set within the cluster, determining that there are u=kvariables if k&#x3e;2, and u=1 variables if k=2, expressing the objective function in terms of the un/k variables, solving the objective function in terms of the un/k variables using a Quadratic Unconstrained Binary Optimization (QUBO) solver to obtain an updated solution, determining whether a convergence criteria is satisfied for the updated solution, and upon a determination that a convergence criteria is satisfied, outputting the updated solution to the objective function.

Local search with global view for large scale combinatorial optimization

A method may include exchanging a secret symmetric key (SSK) between a first trusted execution environment (TEE) of a first system, a second TEE of a second system and a third TEE of a third system. The method may also include receiving, by the first system, an encrypted first set of data from the second system. The method may also include decrypting, by the first TEE, the encrypted first set of data using the SSK. The method may also include receiving, by the first system, an encrypted query from the third system. The method may also include decrypting, by the first TEE, the encrypted query using the SSK. The method may also include determining, by the first TEE, a query result to the decrypted query using index sets. The method may also include sending, by the first TEE, the encrypted query result to the third system.

Searchable sets of data using trusted execution environment

A method may include obtaining a first matrix that represents data in a data set and obtaining a number of clusters into which the data is to be grouped. The method may further include constructing a second matrix using the first matrix and the number of clusters. The second matrix may represent a formulation of a first optimization problem in a framework of a second optimization problem. The method may further include solving the second optimization problem using the second matrix to generate a solution of the second optimization problem and mapping the solution of the second optimization problem into a first solution matrix that represents a solution of the first optimization problem. The method may further include grouping the data into multiple data clusters using the first solution matrix. A number of the multiple data clusters may be equal to the number of clusters.

Data clustering

A method may include receiving, from a first trusted authority, a secret key specific to a party for use in posting to a blockchain. The method may also include receiving, from a second trusted authority, a correlated randomness component specific to the party and associated with a given temporal segment. The method may additionally include generating a party-generated randomized mask, and computing, using an input from the party, the correlated randomness component, and the party-generated randomized mask in a non-interactive multi-party computation (NIMPC), an NIMPC-encrypted input associated with the party for the given temporal segment. The method may also include encrypting the NIMPC-encrypted input according to a blockchain encryption algorithm to yield a ciphertext, and submitting the ciphertext to a block associated with the given temporal segment in a blockchain.

Selectively private distributed computation for blockchain

A method of participation verification includes generating sets of cryptocurrency coins (coin sets). The coin sets have cryptocurrency coins and correlate to events for which participation is verified. The method includes generating user keys including unique public keys for each user and user secret keys. The method includes assigning a public key to a user, communicating the assigned public key to a user device and enabling download of a verification application. The method includes receiving a first coin request that includes identification of a first coin set, the assigned public key, and a data set. The method includes verifying user participation in an event based on the data set. The method includes executing a cryptocurrency transaction with the user device. The cryptocurrency transaction including public validation of a transfer of a cryptocurrency coin from the identified coin set to the user device via an append-only ledger.

Cryptocurrency-based event participation verification

A method may include obtaining a secret key, a user device secret key, and a server secret key based on the secret key and the user device secret key. The method may include dividing the user device secret key into a plurality of user device shares and the server secret key into a plurality of server shares. The method may include distributing the plurality of user device shares to a plurality of user devices and the plurality of server shares to a plurality of service providers. The method may include obtaining a public key based on the secret key. The method may also include publishing the public key. The method may include obtaining a recovery authority secret key and a recovery vault secret key such that a user may recover an account if the user devices and/or the service providers are compromised.

Secure key management

According to some examples, computer-implemented methods to detect a potential privacy violation as a result of a release of a database are described. An example computer-implemented method may include anonymizing a database and calculating a measure of entropy resulting from a release of the anonymized database without releasing the anonymized database. The method may also include determining whether the calculated measure of entropy satisfies a privacy threshold and, responsive to a determination that the calculated measure of entropy satisfies the privacy threshold, releasing the anonymized database. The method may further include, responsive to a determination that the calculated measure of entropy does not satisfy the privacy threshold, not allowing the release of the anonymized database.

Detection and prevention of privacy violation due to database release

A method of transmitting a message via a blockchain network is provided. A method may include encrypting, via a first identity-based encryption (IBE) function, a message to generate a ciphertext. The method may further include transmitting the ciphertext to each node of a plurality of nodes in a blockchain network. Further, the method may include decrypting, via a second IBE function, the ciphertext at each node of the plurality of nodes in the blockchain network after at least one condition is met.

Delayed-access encryption for blockchain field

A method may include obtaining a set of multivariate quadratic polynomials associated with a multivariate quadratic problem and generating an Ising Model connection weight matrix 'W' and an Ising Model bias vector 'b' based on the multivariate quadratic polynomials. The method may also include providing the matrix &#x201c;W&#x201d; and the vector &#x201c;b&#x201d; to an annealing system configured to solve problems written according to the Ising Model and obtaining an output from the annealing system that represents a set of integers. The method may also include using the set of integers as a solution to the multivariate quadratic problem.

Solving multivariate quadratic problems using digital or quantum annealing

A method may include assigning each node of a first network to a different node cluster such that a number of nodes equals a number of node clusters, selecting multiple nodes of the first network as a set of nodes, and selecting multiple node clusters as a set of node clusters. The method may also include solving a first optimization problem by reassigning one or more of the nodes of the set of nodes to different node clusters of the set of node clusters while maintaining assigned node clusters of the nodes that are not part of the set of nodes and after reassigning one or more of the nodes of the set of nodes to different node clusters, merging the nodes assigned to at least one of the node clusters to form a second network with fewer nodes than the number of nodes of the first network.

Network node clustering

According to an aspect of an embodiment, operations may include obtaining a basis 'A' that defines a lattice in an m-dimensional space. The operations may further include obtaining a target vector 'y' that defines a particular location in the m-dimensional space. In addition, the operations may include generating an Ising model connection weight matrix &#x201c;W&#x201d; by multiplying a transposition of &#x201c;A&#x201d; (&#x201c;A&#x201d;) by &#x201c;A&#x201d;. Moreover, the operations may include generating an Ising model bias vector &#x201c;b&#x201d; by multiplying a transposition of &#x201c;y&#x201d; (&#x201c;y&#x201d;) by &#x201c;A&#x201d;. The operations may further include providing &#x201c;W&#x201d; and &#x201c;b&#x201d; to an annealing system configured to solve problems written according to the Ising model. Additionally, the operations may include obtaining an output from the annealing system that represents an output vector &#x201c;x&#x201d; of a particular point included in the lattice that is the closest point in the lattice to the particular location defined by &#x201c;y&#x201d;.

Solving lattice problems using annealing

According to an aspect of an embodiment, a method of determining information leakage of a computer-readable program may include obtaining a first component of the computer-readable program. The first component may have a first information leakage that may be unknown. The first component may be comprised of a second component and a third component. The method may also include obtaining a second information leakage of the second component. The method may also include obtaining a third information leakage of the third component. The method may also include determining a relationship between the second component and the third component relative to the first component. The method may also include determining the first information leakage based on the second information leakage, the third information leakage, and the relationship.

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Santa Clara, CA, United States of America

Arnab Roy