WAVEFRONT MUXING AND DEMUXING FOR CLOUD DATA STORAGE AND TRANSPORT

• US 20140081989A1
• Filed: 07/29/2013
• Published: 03/20/2014
• Est. Priority Date: 07/30/2012
• Status: Active Grant

• Current Assignee: Spatial Digital Systems Inc
• Original Assignee: Spatial Digital Systems Inc
• Inventors: Chang, Donald C.D., Chen, Steve K, Lee, Juo-Yu
• Granted Patent: US 10,152,524 B2
• Time in Patent Office: Days
• US Class Current: 707/748
• CPC Class Codes: G06F 11/1076 Parity data used in redundant arrays of independent storages, e.g. in RAID systems
• CPC Class Codes: G06F 11/2058 using more than 2 mirrored copies
• CPC Class Codes: G06F 11/2071 using a plurality of controllers
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 2211/1028 Distributed, i.e. distributed RAID systems with parity
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

Security on data storage and transport are important concerns on cloud computing. Wavefront multiplexing/demultiplexing process (WF muxing/demuxing) embodying an architecture that utilizes multi-dimensional waveforms has found applications in data storage and transport on cloud. Multiple data sets are preprocessed by WF muxing before stored/transported. WF muxed data is aggregated data from multiple data sets that have been “customized processed” and disassembled into any scalable number of sets of processed data, with each set being stored on a storage site. The original data is reassembled via WF demuxing after retrieving a lesser but scalable number of WF muxed data sets. In short, the WF muxed data storage solution enhances data security and data redundancy by, respectively, creating a new dimension to existing security/privacy methods and significantly reducing the storage space needed for data redundancy. In addition, WF muxing/demuxing methods enable a monitoring capability on the integrity of stored data.

Claims

1. A data storage method comprising:

transforming multiple first data sets into multiple second data sets, wherein one of said second data sets comprises a weighted sum of said first data sets; and

storing said second data sets in an IP cloud via Internet.

2. The data storage method of claim 1, wherein the number of said first data sets is equal to the number of said second data sets.

3. The data storage method of claim 1, wherein one of said first data sets comprises a known data set.

4. The data storage method of claim 1, wherein one of said first data sets is grounded.

5. The data storage method of claim 1, wherein said transforming said first data sets into said second data sets comprises performing an orthogonal matrix transform.

6. The data storage method of claim 1, wherein said transforming said first data sets into said second data sets comprises performing a Hadamard transform.

7. The data storage method of claim 1, wherein said transforming said first data sets into said second data sets comprises performing a Fourier transform.

8. The data storage method of claim 1, wherein said transforming said first data sets into said second data sets comprises performing a non-orthogonal full-rank matrix transform.

9. The data storage method of claim 1, wherein said first data sets comprises a redundant data set.

10. A data transport method comprising:

transforming multiple first data sets into multiple second data sets at a transmitting site, wherein one of said second data sets comprises a weighted sum of said first data sets; and

receiving some or all of the second data sets at a receiving site via Internet.

11. The data transport method of claim 10, wherein the number of said first data sets is equal to the number of said second data sets.

12. The data transport method of claim 10, wherein one of said first data sets comprises a known a priori data set.

13. The data transport method of claim 10, wherein one of said first data sets is grounded.

14. The data transport method of claim 10, wherein said transforming said first data sets into said second data sets comprises performing an orthogonal matrix transform.

15. The data transport method of claim 10, wherein said transforming said first data sets into said second data sets comprises performing a Hadamard transform.

16. The data transport method of claim 10, wherein said transforming said first data sets into said second data sets comprises performing a Fourier transform.

17. The data transport method of claim 10, wherein said transforming said first data sets into said second data sets comprises performing a non-orthogonal full-rank matrix transform.

18. The data transport method of claim 10, wherein said first data sets comprises a redundant data set.

19. A method for monitoring data integrity, comprising:

transforming multiple first data sets into multiple second data sets, wherein one of said second data sets comprises a weighted sum of said first data sets;

transforming some or all of said second data sets into multiple third data sets, wherein one of said third data sets comprises a weighted sum of said some or all of said second data sets; and

comparing one of said third data sets to one of said first data sets.

20. The method of claim 19, wherein said one of said first data sets comprises a known data set.