| Patent application number | Description | Published |
|---|
| 20080209226 | User Authentication Via Biometric Hashing - Techniques for authenticating biometric parameters via biometric hashing are described. In one implementation, a biometric parameter of a user (e.g., fingerprint image, blood-vessel pattern, retina scan, etc.) is captured. One or more biometric hashes are produced from the biometric parameter. To generate hashes that appear random, pseudorandom metrics are applied over the biometric parameter. The hashes are stored in association with user information that can be employed to authenticate the user. Subsequently, during authentication, a new biometric parameter is captured and hashes are computed from the parameter. The new biometric hashes are then compared with the predetermined stored hashes. If any of the new hashes are found to be identical, or sufficiently similar, to one or more of the predetermined biometric hashes, the biometric parameter is deemed valid and the user is authenticated. | 08-28-2008 |
| 20080209227 | User Authentication Via Biometric Hashing - Techniques for authenticating biometric parameters via biometric hashing are described. In one implementation, a biometric parameter of a user (e.g., fingerprint image, blood-vessel pattern, retina scan, etc.) is captured. One or more biometric hashes are produced from the biometric parameter. To generate hashes that appear random, pseudorandom metrics are applied over the biometric parameter. The hashes are stored in association with user information that can be employed to authenticate the user. Subsequently, during authentication, a new biometric parameter is captured and hashes are computed from the parameter. The new biometric hashes are then compared with the predetermined stored hashes. If any of the new hashes are found to be identical, or sufficiently similar, to one or more of the predetermined biometric hashes, the biometric parameter is deemed valid and the user is authenticated. | 08-28-2008 |
| 20080235802 | Software Tamper Resistance Via Integrity-Checking Expressions - Implementation of software tamper resistance via integrity checks is described. In one implementation, a tamper resistance tool receives an input program code and generates a tamper-resistant program code using integrity checks. The integrity checks are generated by processing the input program code, and the integrity checks are inserted in various locations in the input program code. Values of the integrity checks are computed during program execution to determine whether a section of the program has been tampered with. Values of the integrity checks may be stored and accessed at any point during execution of the program. | 09-25-2008 |
| 20080320056 | FUNCTION MATCHING IN BINARIES - Which target functions in a target binary have target function basic blocks that match the source function basic blocks in a source function in a source binary is determined. For the target functions having matching target function basic blocks, a target function control flow graph is determined that has the greatest control flow matching strength to a source function control flow graph, wherein a node in the source function control graph represents a source function basic block, wherein a node in a target function control graph represents a target function basic block in a corresponding target function. | 12-25-2008 |
| 20090022308 | Primitives For Fast Secure Hash Functions And Stream Ciphers - Techniques are disclosed to enable efficient implementation of secure hash functions and/or stream ciphers. More specifically, a family of graphs is described that has relatively large girth, large claw, and/or rapid mixing properties. The graphs are suitable for construction of cryptographic primitives such as collision resistant hash functions and stream ciphers, which allow efficient software implementation. | 01-22-2009 |
| 20090326907 | PROGRAM ANALYSIS AS CONSTRAINT SOLVING - Described is a technology by which program analysis uses rich invariant templates that may specify an arbitrary Boolean combination of linear inequalities for program verification. Also described is choosing a cut-set that identifies program locations, each of which is associated with an invariant template. The verification generates second-order constraints, converts second-order logic formula based on those constraints into first-order logic formula, then converts the first-order logic formula into a quantifier-free formula, which is then converted into a Boolean satisfiability formula. Off-the-shelf constraint solvers may then be applied to the Boolean satisfiability formula to generate program analysis results. Various templates may be used to convert the second-order logic formula into the first-order logic formula. Further described are interprocedural analysis and the determination of weakest precondition and strongest postcondition with applications to termination analysis, timing bounds analysis, and generation of most-general counterexamples for both termination and safety properties. | 12-31-2009 |
| 20100088548 | Using Constraint Solving to Discovering Disjunctive and Quantified Invariants Over Predicate Abstraction - Techniques are disclosed for generating complex invariants in a program using a Satisfiability Modulo Theories (SMT) solver. In one embodiment, the generated invariants may be used to validate assert statements in a program. Additionally or alternatively, a weakest pre-condition invariant may be generated such that parameters passed to the program that satisfy the weakest pre-condition are guaranteed to satisfy the program's assert statements. Additionally or alternatively, a strongest post-condition may be generated, determining what is guaranteed to be true about the state of the program upon completion of the program. In one embodiment, the SMT solver generates invariants by mapping predicates onto unknown variables in a template. The template may comprise unknown variables related by logical structures defined with disjunctions, universal quantifiers, and existential quantifiers. The predicates may comprise equalities and inequalities between program variables. | 04-08-2010 |
| 20100106920 | DATA LOCATION OBFUSCATION - Programs running on an open architecture, such as a personal computer, are vulnerable to inspection and modification. This is a concern as the program may include or provide access to valuable information. As a defense, the actual location of data can be hidden throughout execution of the program by way of periodic location reordering and pointer scrambling, among other things. These techniques serve to complicate static data flow analysis and dynamic data tracking thereby at least deterring program tampering. | 04-29-2010 |
| 20100107245 | TAMPER-TOLERANT PROGRAMS - Tamper-tolerant programs enable correct and continued execution despite attacks. Programs can be transformed into tamper-tolerant versions that correct effects of tampering in response to detection thereof Tamper-tolerant programs can execute alone or in conjunction with tamper resistance/prevention mechanisms such as obfuscation and encryption/decryption, among other things. In fact, the same and/or similar mechanisms can be employed to protect tamper tolerance functionality. | 04-29-2010 |
| 20100115260 | UNIVERSAL SECURE TOKEN FOR OBFUSCATION AND TAMPER RESISTANCE - Program obfuscation is accomplished with tamper proof token including an embedded oracle. A public obfuscation function can be applied to any program/circuit to produce a new obfuscated program/circuit that makes calls to the corresponding oracle to facilitate program execution. A universal circuit representation can be employ with respect to obfuscation to hide circuit wiring and allow the whole circuit to be public. Furthermore, the token or embedded oracle can be universal and stateless to enable a single token to be employed with respect to many programs. | 05-06-2010 |
| 20100228809 | Randomized Signal Transforms and Their Applications - Techniques are disclosed to provide randomized signal transforms and/or their applications. More particularly, a signal (e.g., an audio signal, an image, or a video signal) is transformed by applying randomly-selected basis functions to the signal. The applications of the randomized signal transforms include, but are not limited to, compression, denoising, hashing, identification, authentication, and data embedding (e.g., watermarking). | 09-09-2010 |
