Abstract: Described is a system for continuously predicting and adapting optimal strategies for attacker elicitation. The system includes a global bot controlling processor unit and one or more local bot controlling processor units. The global bot controlling processor unit includes a multi-layer network software unit for extracting attacker features from diverse, out-of-band (OOB) media sources. The global controlling processing unit further includes an adaptive behavioral game theory (GT) software unit for determining a best strategy for eliciting identifying information from an attacker. Each local bot controlling processor unit includes a cognitive model (CM) software unit for estimating a cognitive state of the attacker and predicting attacker behavior. A generative adversarial network (GAN) software unit predicts the attacker's strategies.

Abstract: Described is a system for network threat detection. The system identifies a targeted sub-network representing a threat within a multi-layer network having members. The targeted sub-network is identified with differential privacy protection, such that privacy of individuals that are not in the targeted sub-network is protected. The system causes an action to be generated, the action being one of generating an alert of a threat, initiating monitoring of the non-benign persons, or disabling network access of the non-benign persons.

Abstract: Described is a system for selective transparency in a public ledger. In operation, a first submission by a first entity is logged to the public ledger. The submission is a data entry with a message M and an identification number (ID). Separately, a linkage by a second entity is recorded. The linkage is an encryption and commitment linking the submission by the first entity to a second submission by the second entity. The linkage can be verified through a series of processes, such as by determining a value of linkage verification information. The value of the linkage verification information and corresponding block number is then transmitted to a third entity. The third entity reads the commitments from block Ni and verifies that the commitments are commitments to the same ID using the linkage verification information.

Abstract: Described is a system for jointly generating a random value amongst a set of servers for secure data sharing. The set of servers initiates a randomness generation protocol where each server in the set of servers selects a randomly generated polynomial and broadcasts a cryptographic hash function of the randomly generated polynomial. Each server sends its value of the cryptographic hash function of the randomly generated polynomial to the set of servers. The randomness generation protocol is used in a multi-party computation protocol to ensure a set of data is securely shared electronically amongst the set of servers via a secure, authenticated broadcast channel.

Abstract: Described is a low power system for mobile devices that provides continuous, behavior-based security validation of mobile device applications using neuromorphic hardware. A mobile device comprises a neuromorphic hardware component that runs on the mobile device for continuously monitoring time series related to individual mobile device application behaviors, detecting and classifying pattern anomalies associated with a known malware threat in the time series related to individual mobile device application behaviors, and generating an alert related to the known malware threat. The mobile device identifies pattern anomalies in dependency relationships of mobile device inter-application and intra-applications communications, detects pattern anomalies associated with new malware threats, and isolates a mobile device application having a risk of malware above a predetermined threshold relative to a risk management policy.

Abstract: Described is a system for jointly generating a random value amongst a set of servers for secure data sharing. The set of servers initiates a randomness generation protocol where each server in the set of servers selects a randomly generated polynomial and broadcasts a cryptographic hash function of the randomly generated polynomial. Each server sends its value of the cryptographic hash function of the randomly generated polynomial to the set of servers. The randomness generation protocol is used in a multi-party computation protocol to ensure a set of data is securely shared electronically amongst the set of servers via a secure, authenticated broadcast channel.

Abstract: Described is a system for improving data authentication using blockchain technology and multi-party computation (MPC). The system ensures authenticity of distributed data sent from one or more servers to the distributed clients. The system initializes MPC protocols to ensure secrecy of keys used to sign a new data element. Blockchain technology is utilized to ensure correctness and integrity of the new data element. A bidirectional blockchain is used such that a forward blockchain stores the new data element to be received by the distributed clients, and a reverse blockchain stores a public key used by the distributed clients to verify authenticity of the new data element stored in the forward blockchain. Signing of the new data element with the public key causes a previous public key to expire.

Abstract: Described is a system for selective transparency in a public ledger. In operation, a first submission by a first entity is logged to the public ledger. The submission is a data entry with a message M and an identification number (ID). Separately, a linkage by a second entity is recorded. The linkage is an encryption and commitment linking the submission by the first entity to a second submission by the second entity. The linkage can be verified through a series of processes, such as by determining a value of linkage verification information. The value of the linkage verification information and corresponding block number is then transmitted to a third entity. The third entity reads the commitments from block Ni and verifies that the commitments are commitments to the same ID using the linkage verification information.

Abstract: Described is a system for predicting the behavior of an autonomous system. The system identifies a taxonomic state of a motion condition of an autonomous vehicle based on a spatiotemporal location of the autonomous vehicle and elements of a driving scenario. Behavior of the autonomous vehicle is predicted based on the taxonomic state of the motion condition. The autonomous vehicle makes and implements a driving operation decision based on the predicted behavior.

Abstract: Described is a system for improving data authentication using blockchain technology and multi-party computation (MPC). The system ensures authenticity of distributed data sent from one or more servers to the distributed clients. The system initializes MPC protocols to ensure secrecy of keys used to sign a new data element. Blockchain technology is utilized to ensure correctness and integrity of the new data element. A bidirectional blockchain is used such that a forward blockchain stores the new data element to be received by the distributed clients, and a reverse blockchain stores a public key used by the distributed clients to verify authenticity of the new data element stored in the forward blockchain. Signing of the new data element with the public key causes a previous public key to expire.

Abstract: Described is a system for improving data privacy in Internet of Things (IoT) devices. The system includes an IoT device having data stored thereon, one or more blockchain nodes in communication with the IoT device, and one or more multi-party computation (MPC) nodes in communication with the IoT device and the one or more blockchain nodes. The data is encrypted using a blockchain process, and a symmetric key for the encrypted data is securely distributed via a MPC process to a data recipient.

Abstract: Described is a system for cloud-based privacy-preserving navigation operations between multiple parties. The system performs a two-party computation (2PC) between input data related to a current location of a first party and public data stored on a cloud computing infrastructure. Each party individually performs a 2PC on the public data while maintaining privacy of their input data. The system then performs multi-party computations (MPC) between multiple parties and the cloud computing infrastructure. The multiple parties privately update the public data with a result obtained from the 2PC. For the first party, a privacy-preserved navigation result is generated using results obtained from the 2PC and the MPC. The first party is caused to perform a navigation operation based on the privacy-preserved navigation result.

Abstract: Described is a low power system for mobile devices that provides continuous, behavior-based security validation of mobile device applications using neuromorphic hardware. A mobile device comprises a neuromorphic hardware component that runs on the mobile device for continuously monitoring time series related to individual mobile device application behaviors, detecting and classifying pattern anomalies associated with a known malware threat in the time series related to individual mobile device application behaviors, and generating an alert related to the known malware threat. The mobile device identifies pattern anomalies in dependency relationships of mobile device inter-application and intra-applications communications, detects pattern anomalies associated with new malware threats, and isolates a mobile device application having a risk of malware above a predetermined threshold relative to a risk management policy.

Abstract: Described is a system for cloud-based privacy-preserving navigation operations between multiple parties. The system performs a two-party computation (2PC) between input data related to a current location of a first party and public data stored on a cloud computing infrastructure. Each party individually performs a 2PC on the public data while maintaining privacy of their input data. The system then performs multi-party computations (MPC) between multiple parties and the cloud computing infrastructure. The multiple parties privately update the public data with a result obtained from the 2PC. For the first party, a privacy-preserved navigation result is generated using results obtained from the 2PC and the MPC. The first party is caused to perform a navigation operation based on the privacy-preserved navigation result.

Abstract: Described is a system for predicting the behavior of an autonomous system. The system identifies a taxonomic state of a motion condition of an autonomous vehicle based on a spatiotemporal location of the autonomous vehicle and elements of a driving scenario. Behavior of the autonomous vehicle is predicted based on the taxonomic state of the motion condition. The autonomous vehicle makes and implements a driving operation decision based on the predicted behavior.