Abstract: Disclosed is a method, system, and computer program product for determining solvency of a digital asset exchange system. The method includes identifying a plurality of blockchain addresses corresponding to a plurality of users of a digital asset exchange system, generating a first commitment to an amount of digital assets corresponding to the plurality of blockchain addresses, and generating a second commitment to a balance of each user of the plurality of users. The method also includes generating a first component of a zero-knowledge algorithm that is configured to receive, as input, the first commitment. The method further includes generating, with at least one processor, a second component of the zero-knowledge algorithm that is configured to receive, as input, the second commitment. The method further includes determining that the digital asset exchange system is solvent based on the zero-knowledge algorithm.

Abstract: Techniques of authenticating a first device of a user to a second device are disclosed. The method enables the second device to perform authentication using a biometric template stored on the first device and a biometric measurement. Homomorphic encryption may be used by the first device to encrypt the biometric template and the second device to determine an encrypted similarity metric between the biometric template and the biometric measurement. The second device can also determine an encrypted code using an authentication function and the encrypted similarity metric. The second device sends the encrypted code and the encrypted similarity metric to be decrypted by the first device. The second device can receive a response from the first device, indicating whether a decrypted similarity metric exceeds a threshold; and whether the decrypted code matches a test code. The second device can then authenticate the user based on the response.

Abstract: Described herein are systems and techniques for privacy-preserving unsupervised learning. The disclosed system and methods can enable separate computers, operated by separate entities, to perform unsupervised learning jointly based on a pool of their respective data, while preserving privacy. The system improves efficiency and scalability, while preserving privacy and avoids leaking a cluster identification. The system can jointly compute a secure distance via privacy-preserving multiplication of respective data values x and y from the computers based on a 1-out-of-N oblivious transfer (OT). In various embodiments, N may be 2, 4, or some other number of shares. A first computer can express its data value x in base-N. A second computer can form an ×N matrix comprising random numbers mi,0 and the remaining elements mi,j=(yjNi?mi,0) mod . The first computer can receive an output vector from the OT, having components mi=(yxi Ni?mi,0) mod .

Abstract: Systems and methods for threshold authenticated encryption are provided. A collection of cryptographic devices may encrypt or decrypt a message, provided that a threshold number of those devices participate in the encryption process. One cryptographic device may generate a commitment message and transmit it to the other selected devices. Those devices may each perform a partial computation using the commitment message, and transmit the partial computations back to the encrypting or decrypting device. The encrypting or decrypting device may use those partial computations to produce a cryptographic key, which may then be used to encrypt or decrypt the message.

Abstract: Methods are provided for maintaining user privacy, and may include establishing a secret key for communication between a plurality of user devices, the plurality of user devices including a first user device associated with a requesting user and a second user device associated with a second user, wherein at least one server computer does not have access to the secret key; receiving from the first user device, a split-payment request message comprising encrypted data, the encrypted data included in the split-payment request message encrypted based on the secret key; generating an encrypted balance for the requesting user and the second user based on the encrypted data of the split-payment request message; and transmitting to the second user device, a split-payment confirmation message including the encrypted balance for the requesting user and/or the second user. Systems and computer program products are also provided.

Abstract: An initiator device can broadcast a witness request to one or more authentication devices. The one or more authentication devices can then determine an assurance level from a range of assurance levels and determine a token share corresponding to the assurance level. The initiator device can then receive, from the one or more authentication devices, at least one witness response comprising the token share corresponding to the assurance level. The initiator device can generate an authentication token using a set of token shares. The initiator device can then transmit the authentication token to an authentication server, wherein the authentication server verifies the authentication token.

Abstract: Methods are provided for maintaining user privacy, and may include establishing a secret key for communication between a plurality of user devices, the plurality of user devices including a first user device associated with a requesting user and a second user device associated with a second user, wherein at least one server computer does not have access to the secret key; receiving from the first user device, a split-payment request message comprising encrypted data, the encrypted data included in the split-payment request message encrypted based on the secret key; generating an encrypted balance for the requesting user and the second user based on the encrypted data of the split-payment request message; and transmitting to the second user device, a split-payment confirmation message including the encrypted balance for the requesting user and/or the second user. Systems and computer program products are also provided.

Abstract: New and efficient protocols are provided for privacy-preserving machine learning training (e.g., for linear regression, logistic regression and neural network using the stochastic gradient descent method). A protocols can use the two-server model, where data owners distribute their private data among two non-colluding servers, which train various models on the joint data using secure two-party computation (2PC). New techniques support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions, such as sigmoid and softmax.

Abstract: Techniques of authenticating a first device of a user to a second device are disclosed. The method enables the second device to perform authentication using a biometric template stored on the first device and a biometric measurement. Homomorphic encryption may be used by the first device to encrypt the biometric template and the second device to determine an encrypted similarity metric between the biometric template and the biometric measurement. The second device can also determine an encrypted code using an authentication function and the encrypted similarity metric. The second device sends the encrypted code and the encrypted similarity metric to be decrypted by the first device. The second device can receive a response from the first device, indicating whether a decrypted similarity metric exceeds a threshold; and whether the decrypted code matches a test code. The second device can then authenticate the user based on the response.

Abstract: Techniques of authenticating a first device of a user to a second device are disclosed. The method enables the second device to perform authentication using a biometric template stored on the first device and a biometric measurement. Homomorphic encryption may be used by the first device to encrypt the biometric template and the second device to determine an encrypted similarity metric between the biometric template and the biometric measurement. The second device can also determine an encrypted code using an authentication function and the encrypted similarity metric. The second device sends the encrypted code and the encrypted similarity metric to be decrypted by the first device. The second device can receive a response from the first device, indicating whether a decrypted similarity metric exceeds a threshold; and whether the decrypted code matches a test code. The second device can then authenticate the user based on the response.

Abstract: Described herein are systems and techniques for privacy-preserving unsupervised learning. The disclosed system and methods can enable separate computers, operated by separate entities, to perform unsupervised learning jointly based on a pool of their respective data, while preserving privacy. The system improves efficiency and scalability, while preserving privacy and avoids leaking a cluster identification. The system can jointly compute a secure distance via privacy-preserving multiplication of respective data values x and y from the computers based on a 1-out-of-N oblivious transfer (OT). In various embodiments, N may be 2, 4, or some other number of shares. A first computer can express its data value x in base-N. A second computer can form an ×N matrix comprising random numbers mi,0 and the remaining elements mi,j=(yjNi?mi,0) mod . The first computer can receive an output vector from the OT, having components mi=(yxi Ni?mi,0) mod .

Abstract: Embodiments disclosed herein are directed to methods and systems of password-based threshold authentication, which distributes the role of an authentication server among multiple servers. Any t servers can collectively verify passwords and generate authentication tokens, while no t?1 servers can forge a valid token or mount offline dictionary attacks.

Abstract: Disclosed is a method, system, and computer program product for determining solvency of a digital asset exchange system. The method includes identifying a plurality of blockchain addresses corresponding to a plurality of users of a digital asset exchange system, generating a first commitment to an amount of digital assets corresponding to the plurality of blockchain addresses, and generating a second commitment to a balance of each user of the plurality of users. The method also includes generating a first component of a zero-knowledge algorithm that is configured to receive, as input, the first commitment. The method further includes generating, with at least one processor, a second component of the zero-knowledge algorithm that is configured to receive, as input, the second commitment. The method further includes determining that the digital asset exchange system is solvent based on the zero-knowledge algorithm.

Abstract: Described herein are systems and techniques for privacy-preserving unsupervised learning. The disclosed system and methods can enable separate computers, operated by separate entities, to perform unsupervised learning jointly based on a pool of their respective data, while preserving privacy. The system improves efficiency and scalability, while preserving privacy and avoids leaking a cluster identification. The system can jointly compute a secure distance via privacy-preserving multiplication of respective data values x and y from the computers based on a 1-out-of-N oblivious transfer (OT). In various embodiments, N may be 2, 4, or some other number of shares. A first computer can express its data value x in base-N. A second computer can form an ×N matrix comprising random numbers mi,0 and the remaining elements mi,j=(yjNi-mi,0) mod . The first computer can receive an output vector from the OT, having components mi=(yxi Ni-mi,0) mod .

Abstract: Disclosed is a system, method, and computer program product for determining solvency of a digital asset exchange system. The method includes identifying a plurality of blockchain addresses corresponding to a plurality of users of the digital asset exchange system, generating a first commitment to an amount of digital assets corresponding to the plurality of blockchain addresses, generating a second commitment to a balance of each user of the plurality of users, generating a first component of a zero-knowledge algorithm configured to receive, as input, the first commitment, and to output a value generated based on each public key, generating a second component of the zero-knowledge algorithm configured to receive, as input, the second commitment, and to output a value generated based on each user balance, and determining, with at least one processor, that the digital asset exchange system is solvent based on the zero-knowledge algorithm.

Abstract: New and efficient protocols are provided for privacy-preserving machine learning training (e.g., for linear regression, logistic regression and neural network using the stochastic gradient descent method). A protocols can use the two-server model, where data owners distribute their private data among two non-colluding servers, which train various models on the joint data using secure two-party computation (2PC). New techniques support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions, such as sigmoid and softmax.

Abstract: Systems and methods for threshold authenticated encryption are provided. A collection of cryptographic devices may encrypt or decrypt a message, provided that a threshold number of those devices participate in the encryption process. One cryptographic device may generate a commitment message and transmit it to the other selected devices. Those devices may each perform a partial computation using the commitment message, and transmit the partial computations back to the encrypting or decrypting device. The encrypting or decrypting device may use those partial computations to produce a cryptographic key, which may then be used to encrypt or decrypt the message.

Abstract: Embodiments disclosed herein are directed to methods and systems of password-based threshold authentication, which distributes the role of an authentication server among multiple servers. Any t servers can collectively verify passwords and generate authentication tokens, while no t-1 servers can forge a valid token or mount offline dictionary attacks.

Abstract: New and efficient protocols are provided for privacy-preserving machine learning training (e.g., for linear regression, logistic regression and neural network using the stochastic gradient descent method). A protocols can use the two-server model, where data owners distribute their private data among two non-colluding servers, which train various models on the joint data using secure two-party computation (2PC). New techniques support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions, such as sigmoid and softmax.

Abstract: Systems and methods for threshold authenticated encryption are provided. A collection of cryptographic devices may encrypt or decrypt a message, provided that a threshold number of those devices participate in the encryption process. One cryptographic device may generate a commitment message and transmit it to the other selected devices. Those devices may each perform a partial computation using the commitment message, and transmit the partial computations back to the encrypting or decrypting device. The encrypting or decrypting device may use those partial computations to produce a cryptographic key, which may then be used to encrypt or decrypt the message.