# US 12,008,120 B2 · Data distribution and security in a multilayer storage infrastructure

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- Number: US 12,008,120 B2
- Document ID: US12008120B2
- Status: Granted
- Kind: B2
- Application number: 17/339,826
- Filed: 2021-06-04
- Published: 2022-12-08
- Granted: 2024-06-11
- Earliest claimed: 2021-06-04
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Craig M. Trim; Shikhar Kwatra; Bennet Prabhu; Jayabalan Arumugam
- Web page: https://patentgrep.com/patent/US12008120B2

## CPC

- G06F 21/6209: {to a single file or object, e.g. in a secure envelope, encrypted and accessed using a key, or with access control rules appended to the object itself}
- G06F 11/1004: {to protect a block of data words, e.g. CRC or checksum (G06F11/1076 takes precedence; security arrangements for protecting computers or computer systems against unauthorized activity G06F21/00)}
- G06F 18/23: Clustering techniques
- G06F 21/602: {Providing cryptographic facilities or services}
- G06F 40/205: Parsing
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/098: Distributed learning, e.g. federated learning
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 20/20: Ensemble learning
- G06V 30/262: using context analysis, e.g. lexical, syntactic or semantic context
- G06F 40/30: Semantic analysis
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Techniques are described relating to data distribution and security in a multilayer storage infrastructure. An associated computer-implemented method includes receiving file data associated with a user for storage in a managed services domain, applying an ensemble learning model to devise a data distribution technique for the file data based upon contextual information associated with the user, and encrypting the file data. The method further includes, based upon the data distribution technique, dividing the file data to store among a cloud computing layer, a fog computing layer, and a local computing layer by performing a hash transformation and applying at least one cyclic error correcting code. In an embodiment, the method further includes receiving a data access request associated with the file data, authenticating the data access request, and restoring the file data via decryption.

## Claims

### Claim 1

A computer-implemented method comprising:
receiving file data associated with a user for storage in a managed services domain;
applying an ensemble learning model to devise a data distribution technique for the file data based upon contextual information associated with the user, wherein applying the ensemble learning model comprises:
deriving topical context data by applying natural language processing (NLP) to the contextual information; and
deriving a plurality of file data portions by applying NLP to the file data based upon the topical context data;
encrypting the file data; and
based upon the data distribution technique, dividing the file data to store among a cloud computing layer, a fog computing layer, and a local computing layer by performing a hash transformation and applying at least one cyclic error correcting code.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, further comprising:
receiving a data access request associated with the file data;
authenticating the data access request; and
restoring the file data via decryption.

### Claim 3 (depends on 2)

The computer-implemented method of claim 2, wherein authenticating the data access request comprises:
parsing identification and a password from the data access request;
fetching a stored hash value pre-associated with the user based upon the parsed identification;
hashing the password by applying to the parsed password one or more cryptographic hash functions corresponding to the stored hash value; and
responsive to determining that a result of the password hash matches the stored hash value, completing authentication of the data access request.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein applying the ensemble learning model further comprises:
applying at least one multi-class classification technique to the plurality of file data portions.

### Claim 5 (depends on 4)

The computer-implemented method of claim 4, wherein applying the ensemble learning model further comprises:
determining a distribution plan for the plurality of file data portions based upon application of the at least one multi-class classification technique.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein deriving the topical context data comprises:
creating a plurality of encoded feature vectors by applying at least one NLP model to raw data associated with the user; and
obtaining numerical topical output by applying at least one clustering algorithm to the plurality of encoded feature vectors.

### Claim 7 (depends on 6)

The computer-implemented method of claim 6, wherein creating the plurality of encoded feature vectors comprises:
deriving at least one data representation based upon application types or data types accessed by the user.

### Claim 8 (depends on 6)

The computer-implemented method of claim 6, wherein creating the plurality of encoded feature vectors comprises:
deriving at least one data representation pertaining to frequency of application access or data access by the user; and
deriving at least one data representation pertaining to computing resource usage or storage patterns related to application access or data access by the user.

### Claim 9 (depends on 6)

The computer-implemented method of claim 6, wherein a respective feature of one of the plurality of encoded feature vectors is associated with a respective topic based upon the numerical topical output obtained for the respective feature.

### Claim 10 (depends on 1)

The computer-implemented method of claim 1, wherein deriving the plurality of file data portions comprises:
identifying topical patterns among datapoints within the file data by applying at least one NLP model in view of the topical context data; and
allocating the datapoints within the file data to the plurality of file data portions by applying at least one clustering algorithm based upon the identified topical patterns.

### Claim 11 (depends on 1)

The computer-implemented method of claim 1, wherein dividing the file data comprises:
storing separate parts of the file data among the cloud computing layer, the fog computing layer, and the local computing layer.

### Claim 12 (depends on 1)

The computer-implemented method of claim 1, wherein dividing the file data comprises:
storing redundant dependencies among each of the cloud computing layer, the fog computing layer, and the local computing layer.

### Claim 13 (depends on 1)

The computer-implemented method of claim 1, wherein the contextual information includes user frequency of data user or user frequency of application use.

### Claim 14 (depends on 1)

The computer-implemented method of claim 1, wherein the contextual information includes user system configuration or user file storage pattern.

### Claim 15

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:
receive file data associated with a user for storage in a managed services domain;
apply an ensemble learning model to devise a data distribution technique for the file data based upon contextual information associated with the user, wherein applying the ensemble learning model comprises:
deriving topical context data by applying natural language processing (NLP) to the contextual information; and
deriving a plurality of file data portions by applying NLP to the file data based upon the topical context data;
encrypt the file data; and
based upon the data distribution technique, divide the file data to store among a cloud computing layer, a fog computing layer, and a local computing layer by performing a hash transformation and applying at least one cyclic error correcting code.

### Claim 16 (depends on 15)

The computer program product of claim 15, wherein the program instructions further cause the computing device to:
receive a data access request associated with the file data;
authenticate the data access request; and
restore the file data via decryption.

### Claim 17

A system comprising:
at least one processor; and
a memory storing an application program, which, when executed on the at least one processor, performs an operation comprising:
receiving file data associated with a user for storage in a managed services domain;
applying an ensemble learning model to devise a data distribution technique for the file data based upon contextual information associated with the user, wherein applying the ensemble learning model comprises:
deriving topical context data by applying natural language processing (NLP) to the contextual information; and
deriving a plurality of file data portions by applying NLP to the file data based upon the topical context data;
encrypting the file data; and
based upon the data distribution technique, dividing the file data to store among a cloud computing layer, a fog computing layer, and a local computing layer by performing a hash transformation and applying at least one cyclic error correcting code.

### Claim 18 (depends on 17)

The system of claim 17, wherein the operation further comprises:
receiving a data access request associated with the file data;
authenticating the data access request; and
restoring the file data via decryption.