This document describes a study on quantifying privacy levels in publishing smart tap network data. It outlines the background, related works, methodology, results and conclusions. The methodology proposes using entropy, specifically approximate entropy and sample entropy, to quantify the amount of human activity information contained in power consumption data. This "privacy level" is defined as the entropy rate of a data set relative to white noise. The study applies this methodology to analyze smart tap data from the IREF building over 5 weeks, setting parameters like time lag, pattern length m, and threshold r for calculating entropy. The results show entropy rates can help determine safe privacy levels for publishing power consumption data.

1. A Study on Privacy Level in
Publishing Data of Smart Tap Network
The University of Tokyo
Esaki Laboratory
Tran Quoc Hoan 2014.03.18@Niigata
1

2. Outline
1. Background & Purpose
2. Related works
3. Proposal
4. Methodology
5. Result & Discussion
6. Conclusion
2

3. Background & Purpose
• Background
1. Smart tap & Big data
2. Privacy Preserving Data
Publishing (PPDP)
3. Difficulty in anonymising
time series data
• Research purpose
• Using entropy to quantify the
risk of publishing smart tap’s
data
Alice Bob Peter
Original
Dataset
Data
Recipient
DataPublishingDataCollectionDataanonymise
Data
Processor
3

4. Related works
1. Smart Metering & Privacy (Quinn, 2009)
2. Time series chaos analysis in physiology
• Approximate Entropy (Pincus, 1992)
• Bias effect (Ex. random noise)
• Sample Entropy (Richman, 2001)
• Avoiding of bias effect
• Difference from original entropy definition
4

5. 15.556%
31.111%
46.667%
62.222%
Proposal(1): Privacy Level
• “Privacy level” = quantity of human activity
information in power consumption data (%)
Refrigerator
(regularity)
Time points Time points Time points
power value power value power value
White-noise
(irregularity)
Laptop
(???)
Privacylevel
• Evaluation of regularity (entropy)
5

6. 22.222%
44.444%
66.667%
88.889%
Proposal(2): Entropy rate
• Entropy Rate = Entropy(data)/Entropy(white-noise)
1
0
Privacy Level = EnRate
Entropy
rate
Refrigerator
(regularity)
White-noise
(irregularity)
Laptop
(???)
HRate
LRate
Time points Time points Time points
power value power value power value
Publish Safe
Publish Safe
6

7. Proposing Methodology
1. Decide parameters for entropy calculation
• Time lag, m, r
2. Calculate entropy value, entropy rate
3. Decide LRate, HRate and privacy level
• Using Approximate Entropy (ApEn) & Sample Entropy (SaEn)
7

8. Parameters for entropy calculation
8
0
15
30
45
60
Ex. lag = 1, m=3
• Time series x[1], x[2], …, x[N]
• pattern i: (x[i],x[i+lag],…,x[i+(m-1)lag])
• m: number of data points in pattern
• lag: sampling interval in pattern
• dis(i,j)=max(|x[i+(p-1)lag]-x[j+(p-1)lag]|, p=0,m-1)
• r: dis(i,j) ≤ r → pattern i ∼ pattern j
pattern i j ki j ki
8

9. Entropy Calculation
• A(i): number of pattern k similar with pattern i ( k != i)
• B(i): number of pattern (k+lag) similar with pattern (i+lag)
Bias when A(i)=B(i)=0 (random noise)
ー ー
0
15
30
45
60
Time points
Ex. lag = 1, m=3
j ki
i+lag
j+lag k+lagi+lag
9

10. Setting time lag
First ACF zero-crossing lag = 7
ApEn = 1.223; SaEn = 0.944
First ACF zero-crossing lag = 198
ApEn = 1.299; SaEn = 1.457
10

11. Setting m, r
Choose m, r satisfy
95%Confidence Interval of
the Estimate ≤ 10%SaEn
White-noise Entropy
Choose m, r maximum ApEn
バイアス
領域
std: standard deviation
m=2,3
r=0,1->0.4
11

12. Evaluation
1. Learning data set (for setting m, r)
• Tracebase (tracebase.org) (138 devices)
• m=2,3; r=0.1→0.4
2. Evaluation data set
• IREF Building 2F-5F (136 devices, 5 weeks)
12

13. IREF 136 devs EnRate (5 weeks)
SaEnRate
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ApEnRate
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Result (1)
m=2, r=0.2*standard deviation
13

14. Result (2)IREF Laptop EnRate (11 devs, 5 weeks)
SaEnRate
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
ApEnRate
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
HRate
LRate
LRate HRate
LRate = Mean - Standard Deviation
HRate = Mean + Standard Deviation
Warning
14

15. Discussion
1. Entropy is sensitive to data sets that include outliers
2. Relation between entropy and privacy of data
3. Future work
• Calculate entropy with meaning patterns
• Using entropy for other knowledge (device classification,
abnormal pattern detection,…)
• Privacy Preserving Protocol
15

16. Conclusion
1. Quantified the human activity
information included in smart-taps’ data
2. Applied entropy in physiology (ApEn,
SaEn) to power consumption data
3. Defined entropy rate to determine
privacy level of published power
consumption data
16

17. A Study on Privacy Level in
Publishing Data of Smart Tap Network
Esaki Laboratory
zoro@hongo.wide.ad.jp
Thank you for listening !
17

18. Backup slides
18

19. Demand and Supply
1. Demand Oriented Approach of Power Grid
• Supply matches volatile demand
• Supply side is volatile as well
2. Bi-directional communication (Internet of Things)
• Anticipate future supply/demand
• Shape demand, supply-oriented
• Personal data is needed for effective demand side management
19

20. Risk of Privacy Abuse
20
Inference forward channel
Inference backward channel
By consumption patterns
• Appliance detection
• Use mode detection
• Behavior deduction
By demand response data
• Incentive sensitivity
• Customer preference
Household Managements
Data collectors
Ex. Behavior Patterns:
• Washing (10h-12h)
• TV (19h-23h)
• Out (12h-18h)

21. The Concept of EU for Privacy
21
Discriminator
Machine learning
x
Pseudonym
Consumption Data
non-identifying
information
identifying
information
Pseudonymization
Template Data
Source: “Privacy in the Smart Energy Grid”,
Lecture at NII 2014-03-13, Prof. Gunter Muller

22. Service Feedback Loop
22
Household
Service Provider
Billing
Aggregation
Compliance Verification
Data collectors
• Bill
• Consumption Target
Consumption
trace
(My research)
Privacy level = (??)%
Query
Privacy Preserving
Protocol
$$$
Future work
Encryption
Service Provider
Billing
Aggregation
Compliance Verification
Service Provider
Billing
Aggregation
Compliance Verification

23. Privacy Preserving Query Scenario
23
Q1. How many people have energy consumption between 19h-20h which
is over the average ?
Q2. How many people have energy consumption between 19h-20h which
is over the average except Tanaka ?
None-privateQ1: 125, Q2: 124
Attacker
Detection
Privacy preservingQ1: 125, Q2: 127
Service
Provider
Data
Collectors

24. Evaluation System
Time series
segmentation
Real Event
Mapping
Quantify
Privacy Level
24

25. Linkage Attack
in: 9h-10h, 13h-13h30
out: 10h-13h, 13h30-
in: 12h-14h, 16h-18h
out: 18h-
peak: 16h-18hCategorization
Alice Bob Peter
Third party
information
3 people in the room: Alice, Bob, Peter
Peter has printer, Alice has monitor, Bob has PC
Published
Data
Identify
25

26. Regularity in Time Series
• Linear method can’t solve problem => Nonlinear Analysis
Refrigerator data
and its surrogate
ACF and periodgram
Time points
26

27. Entropy (1)
• Display time series data in phase-space
y(m,t) = [x(t), x(t+lag), …, x(t+(m-1)lag)]
• Approximate Entropy (ApEn) and Sample Entropy (SaEn):
evaluate trajectory matching conditional probability
x(t+7)
x(t)
x(t)
x(t+7)
x(t+14)
m=2, lag=7 m=3, lag=7
27

28. Setting time lag
• Time lag = First zero-crossing of ACF
Dev Lag ApEn SaEn
Unknown 500 0.348 0.473
Refri 7 1.223 0.944
Laptop 198 1.299 1.457
Noise 2 3.025 3.247
28

29. Setting m, r (1)
29
• K_A, K_B : overlapped template matching patterns
number (pattern length m, m+1)
• 95% Confidence of SaEn

30. Setting m, r (2)
m=2, r=(0.1~0.4)std
training for parameters: tracebase dataset
30

31. Experiment
Data set Tracebase IREF
Smart tap type Plugwise Plugwise
Number of devices 138 136
Time range Variation 5 weeks
Sampling interval 1 s 2 mins
Usage Training for m, r Evaluation
Result
m=2,3
r=0.1~0.4 std
***
31

32. Result (1)
IREF : 136 devices, 5 weeks
32

33. Other knowledge from entropy rate (?)
Device classification & abnormal detection