Imation Corporation

Proofpoint, Inc.

Ciphertrace, Inc.

Axway Inc.

Tumbleweed Communications Corp.

Marble Security, Inc.

Marblecloud, Inc.

Ironkey, Inc.

Datalocker Inc.

David Alexander Jevans

Gil Spencer

Suresh Kumar Basandra

Rudi Cilibrasi

Shannon Holland

Steve Ryan

Stephen Ryan

Eric R Smith

Provided herein are exemplary systems and methods for creating a secure self-validating network of blockchain/distributed ledger participants. Some exemplary mechanisms support self-validation, mutual-validation, external-validation and privacy controls. Such mechanisms enable the deployment and continued operation of large scale blockchain and distributed ledger systems with a self-certifying security system. They create the ability for rules to be codified to control the rights, privileges and access of nodes depending on their self-certification and external-certification. Also provided is an audit trail of these certifications which can be used for liability claims, insurance, security analytics and forensics.

Verification systems for blockchains and distributed ledgers

Cryptocurrency based malware and ransomware detection systems and methods are disclosed herein. An example method includes analyzing a plurality of malware or ransomware attacks to determine cryptocurrency payment address of malware or ransomware attacks, building a malware or ransomware attack database with the cryptocurrency payment addresses of the plurality of malware or ransomware attacks, identifying a proposed cryptocurrency transaction that includes an address that is included in the malware or ransomware attack database, and denying the proposed cryptocurrency transaction.

Cryptocurrency based malware and ransomware detection systems and methods

Systems and methods of forensic analysis of cryptocurrency transactions are described herein. A method can include obtaining fiat-based transaction data from a bank account, identifying a purchase of a cryptocurrency from fiat-based transaction data and cryptocurrency exchange trade history data from a cryptocurrency exchange where the cryptocurrency was purchased, obtaining cryptocurrency-based transaction data that identifies downstream cryptocurrency transaction data where the cryptocurrency was transferred out of the cryptocurrency exchange; and scoring a user who purchased or used the cryptocurrency based on the fiat-based transaction data, the cryptocurrency exchange trade history data, and the cryptocurrency-based transaction data.

Systems and methods of forensic analysis of cryptocurrency transactions

Systems and methods for automatically searching crypto currency transaction paths and discovering transaction flows between individuals and identifiable services, trim or prune out irrelevant transactions and addresses, and present the relevant information as an identifiable transaction chain are provided herein. In various embodiments the present technology dramatically increases the productivity of investigators and auditors researching crypto currency transactions.

Systems and methods for crypto currency automated transaction flow detection

Systems and methods for analyzing applications ('apps') on a mobile device for security risks for a company while maintaining the mobile device owner's privacy and confidentiality concerning the applications. The mobile device may be a user's personal device (a 'bring your own device'). In an example method, a process generates one or more cryptographic representations of application information for each application on the mobile device. The cryptographic representations may comprise a hash or composite hash. The cryptographic representations may be transmit outside the mobile device to a system which makes a determination and provides an indication whether the application is permitted or not permitted for use at the company. The company can be associated with a hashed permitted or not permitted list. The application information can include application name, executable code, and a version number. The method may include automatically remediating the application if it matches a known risk.

Systems and methods for security analysis of applications on user mobile devices while maintaining user application privacy

Application security analysis including systems and methods for analyzing applications for risk is provided. In an example method, the applications reside on a mobile device configurable to access an enterprise system. The example method includes evaluating each of a plurality of applications variously for privacy, data leakage, and malicious behavior. The example method also includes calculating a risk score for each of the plurality of applications based on the evaluating; and automatically remediating (e.g., quarantining) the applications, of the plurality of applications, for which the risk score meets or exceeds a risk score threshold. The method may evaluate all of the applications residing on a mobile device. The method may include grouping application behaviors, for each of the applications, that indicate an increased risk into groups comprising two or more of privacy risk, a data leakage risk, an account takeover risk, a device takeover risk, and a malware risk.

Methods and systems for security analysis of applications on mobile devices brought into an enterprise network environment

Distributed security mechanism for blockchains and distributed ledgers

Application security analysis

Systems and methods for analyzing applications on a mobile device for risk so as to maintain the privacy of the application user are provided. In the example method, the process receives a request from a mobile device. The request includes a cryptographic representation of application information for an application residing on a mobile device. The method includes comparing the cryptographic representation to an application information database that includes cryptographic representations of applications. The method also includes automatically remediating, e.g., quarantining and retiring, the application if the application matches an application that is a known risk in the database. Exemplary embodiments provide companies with controls to prevent specific applications&#x2014;which have specific behaviors and are present on mobile devices being used by employees&#x2014;from being used by employees, without the company having any visibility into what particular applications are being used by the employees on the mobile device.

Privately performing application security analysis

Secure bulk messaging mechanism in which, roughly described, a sender first encrypts a message once. The message can be decrypted with a message decryption key. These can be symmetric or asymmetric keys. For each recipient, the sender then encrypts the message decryption key with the recipient's public key. The sender then sends the encrypted message and the encrypted message decryption keys to a store-and-forward server. Subsequently, one or more recipients connect to the server and retrieve the encrypted message and the message encryption key that has been encrypted with the recipient's public key. Alternatively, the server can forward these items to each individual recipient. The recipient then decrypts the encrypted message decryption key with the recipient's private key, resulting in an un-encrypted message decryption key. The recipient then decrypts the message using the un-encrypted message decryption key.

Mechanism for efficient private bulk messaging

Systems and methods for analyzing applications for risk are provided. In an example method, the applications reside on a mobile device that is configurable to access an enterprise system. The example method includes evaluating each of a plurality of applications variously for privacy, data leakage, and malicious behavior. The example method also includes calculating a risk score for each of the plurality of applications based on the evaluating; and automatically remediating (e.g., quarantining) the applications, of the plurality of applications, for which the risk score meets or exceeds a risk score threshold. The method may evaluate all of the applications residing on a mobile device. The method may include grouping application behaviors, for each of the applications, that indicate an increased risk into groups comprising two or more of privacy risk, a data leakage risk, an account takeover risk, a device takeover risk, and a malware risk.

Growing community of inventors

Menlo Park, CA, United States of America

David Alexander Jevans