Memory allocation on non-volatile storage

    公开(公告)号:US10482007B2

    公开(公告)日:2019-11-19

    申请号:US15370566

    申请日:2016-12-06

    Applicant: NOBLIS, INC.

    Inventor: Tyler W. Barrus

    Abstract: Techniques for allocating memory on non-volatile storage mediums, rather than on RAM storage mediums, are provided. In some embodiments, first functions in program code for allocating memory on RAM storage are replaced with corresponding second functions for allocating memory on non-volatile storage. Library files corresponding to the second functions may be stored in programming language libraries, such that the second function may be defined in order to allocate memory on non-volatile storage. In some embodiments, a library file for allocating memory on RAM storage may be modified such that it instead causes allocation of memory on non-volatile storage. Allocating memory, storing data in memory, or retrieving data in memory may, in some embodiments, include providing instructions for a processor to communicate via a bus associated with non-volatile storage rather than a bus associated with RAM storage.

    Rapid genomic sequence classification using probabilistic data structures

    公开(公告)号:US11037654B2

    公开(公告)日:2021-06-15

    申请号:US15977667

    申请日:2018-05-11

    Applicant: NOBLIS, INC.

    Abstract: Techniques for identifying and/or classifying genomic information are provided. In some embodiments, genomic information may be identified by computing systems without access to a database of reference genomic information, instead relying on locally stored probabilistic data structures representing reference genomic information. Query genomic data, such as data taken from a read-set, may be divided into sub-strings, and each of the locally-stored probabilistic data structures may be queried by each of the extracted sub-strings, generating probabilistic outputs indicating either that (a) the sub-string is probably included in the set of data represented by the probabilistic data structure or (b) the sub-string is definitely not included in the set of data. Based on the number and/or proportion of sub-strings from a read-set that are indicated as being likely represented by a probabilistic data structure, a likely identity or classification for the genomic information in the read-set may be determined.

    Secure communication of sensitive genomic information using probabilistic data structures

    公开(公告)号:US11094397B2

    公开(公告)日:2021-08-17

    申请号:US15977646

    申请日:2018-05-11

    Applicant: NOBLIS, INC.

    Inventor: Tyler W. Barrus

    Abstract: Techniques for securely encoding, communicating, and comparing genomic information using probabilistic data structures are provided. In some embodiments, genomic information in a secure computing environment may be encoded and/or anonymized by building a probabilistic data structure that represents sub-strings of the genomic information as members of a set; the probabilistic data structure may then be securely transmitted outside the secure computing environment. In some embodiments, a probabilistic data structure representing sub-strings of sensitive genomic information as members of a set may be received in an unsecure computing environment and may be queried to generate output data indicating whether reference sub-strings are probable members of the set. In some embodiments, querying the probabilistic data structure, and other techniques of analyzing the probabilistic data structure, may be used to determine whether the sensitive genomic information corresponds to an organism associated with the reference genomic information.

    Data recovery through reversal of hash values using probabilistic data structures

    公开(公告)号:US11055399B2

    公开(公告)日:2021-07-06

    申请号:US16253950

    申请日:2019-01-22

    Applicant: NOBLIS, INC.

    Abstract: Systems and methods for recovering passwords from a hash value input are provided. A password space may be segmented into password sets, and a digest set may be generated for each password set. Probabilistic data structures representing the digest sets may be generated. One of the probabilistic data structures may be queried with the hash value input to determine whether the hash value input is likely included in the digest sets. In response to the hash value input being determined to be likely included in the digest set, the passwords constituting the password set corresponding to the digest set may be regenerated, and the hash values constituting the digest set may be regenerated. The generated hash values may be compared to the hash value input to determine a hash value from the digest set that matches the hash value input to recover the password associated with the matched hash value.

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