AUTHENTICATED DOWN-SAMPLING OF TIME-SERIES DATA

• US 20160306373A1
• Filed: 04/16/2015
• Published: 10/20/2016
• Est. Priority Date: 04/16/2015
• Status: Abandoned Application

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: MASHIMA, Daisuke
• Application Number:
  US14/688,945
• Publication Number:
  US 20160306373A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01R 22/063 related to remote communication
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

A method of down-sampling time-series data may include receiving energy usage data representative of energy usage of a customer during a specified period. The energy usage data may be signed with a digital signature of a utility. The method may also include receiving input from a customer effective to select a granularity level to apply to the energy usage data and the energy usage data may be down-sampled to apply the selected granularity level to the energy usage data and produce down-sampled energy usage data. The method may also include communicating the down-sampled energy usage data and hash values of child nodes corresponding to the down-sampled energy usage data to a third party.

Claims

1. A method to down-sample time-series data that includes energy usage data, the method comprising:

receiving energy usage data representative of energy usage of a customer during a specified period, the energy usage data being signed with a digital signature of a utility;

receiving input from a customer effective to select a granularity level to apply to the energy usage data;

down-sampling the energy usage data to apply the selected granularity level to the energy usage data and produce down-sampled energy usage data; and

communicating the down-sampled energy usage data and hash values of child nodes corresponding to the down-sampled energy usage data to a third party.

2. The method of claim 1, wherein the down-sampling comprises aggregating data values pertaining to a common parent node.

3. The method of claim 2, further comprising:

receiving input from a customer effective to select a data block of the energy usage data;

redacting a selected data block from the energy usage data in response to the input;

calculating a hash value for the redacted data block; and

replacing, in the energy usage data, the redacted data block with the calculated hash value corresponding to the redacted data block.

4. The method of claim 3, wherein the hash value for the redacted data block is calculated using at least one of a per-customer key that is unique to the customer, an initialization vector, or a counter.

5. The method of claim 1, further comprising:

receiving input from a customer effective to select a section of data blocks of the energy usage data, the selected section of data blocks including first and second data blocks with a common parent node, wherein the first and second data blocks are sequential;

redacting the selected section of data blocks from the energy usage data in response to the input;

calculating hash values for the first and second data blocks in the selected section of data blocks;

calculating a parent hash value for the common parent node based on a concatenation of hash values of the first and second data blocks; and

replacing the selected section with the parent hash value.

6. The method of claim 5, further comprising communicating to the third party the energy usage data including the parent hash value as a replacement for the selected section.

7. The method of claim 5, wherein the parent hash value for the common parent node is calculated according to an equation:

H_P=H(D_S,H(L,R))

in which;

D_S represents a sum of all data values pertaining to the common parent node, the data values pertaining to the common parent node including data values of the first and second data blocks;

H(L,R) includes a hash value based on a concatenation of hash values of child nodes of the common parent node, the child nodes including the hash values of the first and second data blocks; and

H(D_S, H(L,R)) includes a hash of D_S and H(L,R).

8. The method of claim 1, wherein:

the receiving input comprises receiving input effective to select a first granularity level and a different second granularity level to apply to the energy usage data; and

the down-sampling comprises down-sampling the energy usage data to produce down-sampled energy usage data at the first granularity level and the different second granularity level.

9. A device to down-sample time-series data that includes energy usage data, the device comprising:

a non-transitory computer-readable medium having computer instructions stored thereon; and

a processor communicatively coupled to the non-transitory computer-readable medium and configured to execute the computer instructions to perform or control performance of operations comprising;

receiving energy usage data representative of energy usage of a customer during a specified period, the energy usage data being signed with a digital signature of a utility;

receiving input from a customer effective to select a granularity level to apply to the energy usage data;

down-sampling the energy usage data to apply the selected granularity level to the energy usage data and produce down-sampled energy usage data; and

communicating the down-sampled energy usage data and hash values of child nodes corresponding to the down-sampled energy usage data to a third party.

10. The device of claim 9, wherein the down-sampling comprises aggregating data values pertaining to a common parent node.

11. The device of claim 10, the operations further comprising:

receiving input from a customer effective to select a data block of the energy usage data;

redacting a selected data block from the energy usage data in response to the input;

calculating a hash value for the redacted data block; and

replacing, in the energy usage data, the redacted data block with the calculated hash value corresponding to the redacted data block.

12. The device of claim 11, wherein the hash value for the redacted data block is calculated using at least one of a per-customer key that is unique to the customer, an initialization vector, or a counter.

13. The device of claim 9, the operations further comprising:

receiving input from a customer effective to select a section of data blocks of the energy usage data, the selected section of data blocks including first and second data blocks with a common parent node, wherein the first and second data blocks are sequential;

redacting the selected section of data blocks from the energy usage data in response to the input;

calculating hash values for the first and second data blocks in the selected section of data blocks;

calculating a parent hash value for the common parent node based on a concatenation of hash values of the first and second data blocks; and

replacing the selected section with the parent hash value.

14. The device of claim 13, the operations further comprising communicating to the third party the energy usage data including the parent hash value as a replacement for the selected section.

15. The device of claim 13, wherein the parent hash value for the common parent node is calculated according to an equation:

H_P=H(D_S,H(L,R))

in which;

D_S represents a sum of all data values pertaining to the common parent node, the data values pertaining to the common parent node including data values of the first and second data blocks;

H(L,R) includes a hash value based on a concatenation of hash values of child nodes of the common parent node, the child nodes including the hash values of the first and second data blocks; and

H(D_S, H(L,R)) includes a hash of D_S and H(L,R).

16. The device of claim 9, wherein:

the receiving input comprises receiving input effective to select a first granularity level and a different second granularity level to apply to the energy usage data; and

the down-sampling comprises down-sampling the energy usage data to produce down-sampled energy usage data at the first granularity level and the different second granularity level.

17. A method of generating digitally-signed time-series data that includes energy usage data, the method comprising:

receiving time-series data;

sorting the time-series data sequentially according to timestamps of individual data values of the time-series data;

generating a data structure that includes a plurality of leaf nodes, a plurality of common parent nodes; and

a root node; and

generating a digital signature of the energy usage data from the root node and a private key of a data issuer;

wherein after down-sampling of the time-series data including aggregating two or more of the individual data values into one or more aggregated data values, an integrity and authenticity of the down-sampled time-series data is verifiable with one or more of the digital signature, the aggregated data values, the down-sampled time-series data, hash values of child nodes corresponding to the down-sampled time-series data, or a public key of the data issuer that corresponds to the private key.

18. The method of claim 17, wherein the integrity and authenticity of the down-sampled time-series data is verifiable without the two or more of the individual data values aggregated into the one or more aggregated data values.

19. The method of claim 17, wherein the generating the data structure comprises:

calculating the plurality of leaf nodes by calculating hash values of the individual data values;

calculating the plurality of common parent nodes by, for each common parent node, calculating a hash value of a sum of all individual data values pertaining to the common parent node and of a hash value of a concatenation of hash values of two or more child nodes of the common parent node; and

calculating the root node by calculating a hash value of a sum of all of the individual data values in the time-series data and of a hash value of a concatenation of hash values of two or more child nodes of the root node, wherein the hash values of the two or more child nodes of the root node include hash values of two or more of the plurality of common parent nodes that are at a level of the data structure immediately beneath the root node.

20. The method of claim 17, wherein the data issuer comprises a utility that distributes energy and the receiving comprises receiving energy usage data from a meter at a site to which energy is distributed by the utility, the energy usage data including meter readings generated by the meter.