Abstract:
Techniques for mitigating the transitive data problem using a secure asset manager are provided. These techniques include generating a secure asset manager compliant application by tagging source code for the application with a data tag to indicate that a data element associated with the source code is a sensitive data element, accessing a policy file comprising transitive rules associated with the sensitive data element, and generating one or more object files for the application from the source code. These techniques also include storing a sensitive data element in a secure memory region managed by a secure asset manager, and managing the sensitive data element according to a policy associated with the sensitive data element by an application from which the sensitive data element originates, the policy defining transitive rules associated with the sensitive data element.
Abstract:
A computing device may use machine learning techniques to determine whether a side channel attack is underway and perform obfuscation operations (e.g., operations to raise the noise floor) or other similar operations to stop or prevent a detected side channel attack. The computing device may determine that a side channel attack is underway in response to determining that the computing device is in airplane mode, that the battery of the computing device the battery has been replaced with a stable DC power supply, that the touch-screen display of the computing device has been disconnected, that there are continuous calls to a cipher application programming interface (API) using the same cipher key, that there has been tampering with a behavioral analysis engine of the computing device, or any combination thereof.
Abstract:
A computing device may use machine learning techniques to determine the level, degree, and severity of its vulnerability to side channel attacks. The computing device may intelligently and selectively perform obfuscation operations (e.g., operations to raise the noise floor) to prevent side channel attacks based on the determined level, degree, or severity of its current vulnerability to such attacks. The computing device may also monitor the current level of natural obfuscation produced by the device, determining whether there is sufficient natural obfuscation to prevent a side channel attack during an ongoing critical activity, and perform the obfuscation operation during the ongoing critical activity and in response to determining that there is not sufficient natural obfuscation to adequately protect the computing device against side channel attacks.
Abstract:
Various operations may be performed based on a distance-related function associated with two or more devices. For example, an association procedure for two or more devices may be based on one or more determined distances. Similarly, presence management may be based on one or more determined distances. A distance-related function may take various form including, for example, a distance between devices, two or more distances between devices, a rate of change in a relative distance between devices, relative acceleration between devices, or some combination of two or more of the these distance-related functions.
Abstract:
A method of implementing security in a modular exponentiation function for cryptographic operations is provided. A key is obtained as a parameter when the modular exponentiation function is invoked. The key may be one of either a public key or a private key of a cryptographic key pair. Within the modular exponentiation function, the method ascertains whether the key is greater than L bits long, where L is a positive integer. A countermeasure against an attack is implemented if the key is greater than L bits long. The countermeasure may include one or more techniques (e.g., hardware and/or software techniques) that inhibit or prevent information about the key from being ascertained through analysis. One or more exponentiation operations may then be performed using the key. The same modular exponentiation function may be used to perform encryption and decryption operations but with different keys.
Abstract:
A computing device may use machine learning techniques to determine whether a side channel attack is underway and perform obfuscation operations (e.g., operations to raise the noise floor) or other similar operations to stop or prevent a detected side channel attack. The computing device may determine that a side channel attack is underway in response to determining that the computing device is in airplane mode, that the battery of the computing device the battery has been replaced with a stable DC power supply, that the touch-screen display of the computing device has been disconnected, that there are continuous calls to a cipher application programming interface (API) using the same cipher key, that there has been tampering with a behavioral analysis engine of the computing device, or any combination thereof.
Abstract:
A computing device may use machine learning techniques to determine the level, degree, and severity of its vulnerability to side channel attacks. The computing device may intelligently and selectively perform obfuscation operations (e.g., operations to raise the noise floor) to prevent side channel attacks based on the determined level, degree, or severity of its current vulnerability to such attacks. The computing device may also monitor the current level of natural obfuscation produced by the device, determining whether there is sufficient natural obfuscation to prevent a side channel attack during an ongoing critical activity, and perform the obfuscation operation during the ongoing critical activity and in response to determining that there is not sufficient natural obfuscation to adequately protect the computing device against side channel attacks.
Abstract:
Various operations may be performed based on a distance-related function associated with two or more devices. For example, an association procedure for two or more devices may be based on one or more determined distances. Similarly, presence management may be based on one or more determined distances. A distance-related function may take various form including, for example, a distance between devices, two or more distances between devices, a rate of change in a relative distance between devices, relative acceleration between devices, or some combination of two or more of the these distance-related functions.
Abstract:
A method of implementing security in a modular exponentiation function for cryptographic operations is provided. A key is obtained as a parameter when the modular exponentiation function is invoked. The key may be one of either a public key or a private key of a cryptographic key pair. Within the modular exponentiation function, the method ascertains whether the key is greater than L bits long, where L is a positive integer. A countermeasure against an attack is implemented if the key is greater than L bits long. The countermeasure may include one or more techniques (e.g., hardware and/or software techniques) that inhibit or prevent information about the key from being ascertained through analysis. One or more exponentiation operations may then be performed using the key. The same modular exponentiation function may be used to perform encryption and decryption operations but with different keys.
Abstract:
Systems and methods for providing accelerated passphrase verification are disclosed. In one embodiment, a method includes receiving a full security string, generating a full security string hash code, storing the full security string hash code in a memory, determining at least one substring based on an entropy value associated with one or more leading characters in the full security string, generating at least one substring hash code and at least one corresponding character count value, such that the corresponding character count value equals a number of characters in the at least one substring, and storing the at least one substring hash code and the at least one corresponding character count value in the memory.