1. Multimedia Content Identification Through Smart
Multimedia Content Identification Through Smart Meter Power Usage Profiles
Ulrich Greveler, Peter Gl¨osek¨otterz, Benjamin Justusy, Dennis Loehry
PRESENTED BY: KIRAN KUMAR SURAPATHI
DATE: 03/30/2017

2. OVERVIEW INTRODUCTION RELATED WORK EXPERIMENTAL RESULTS
DATA TRANSMISSION
TV/FILM DETECTION
CONCLUSION

3. I. INTRODUCTION Advanced metering devices (smart meters)
Electrical meter that records consumption of electrical energy at intervals
Installed throughout electric networks
Communicating with central server
Problems regarding high-resolution energy consumption data
Unprecedented invasions of consumer privacy
Intrusive identification
Monitoring of equipment within consumers’ homes
Examples like TV set, refrigerator, toaster, and oven

4. I. INTRODUCTION MAIN CONTRIBUTIONS:
The data transmitted via the Internet by the smart meter are unsigned and unencrypted
5 minutes-chunk of consecutive viewing without major interference by other appliances is sufficient to identify the content
Identifying what channel the TV set in the household was displaying by analyzing the household’s electricity usage profile at a 0.5s sample rate

5. II. RELATED WORK Nonintrusive load monitoring (NILM)
The number of electric appliances in a private home can be identified by their load signatures with impressive accuracy
Currently two approaches of implementing privacy preserving smart meter data analysis
Masking the meter readings
Homomorphic encryption

6. III. EXPERIMENTAL RESULTS
AIMS
What are the possible ways of obtaining and evaluating data coming from a calibrated smart meter?
What can be deduced from smart meter data regarding a person’s TV watching habit in a private home?

7. IV. DATA TRANSMISSION
Transmission of smart meter data to the Server is done through the TCP/IP protocol
Contrary to provider's claim
The smart meter data are not encrypted and unsigned
The unencrypted data can be easily hacked out.
Attacker can send different power consumption data to the server

8. IV. DATA TRANSMISSION

9. IV. DATA TRANSMISSION
Web browser based view on the power consumption profile.
Java-script based application

10. V. TV/FILM DETECTION Television Hardware
Tests were performed on an home LCD television set
Total amount of visible brightness of a picture is a combination of the backlighting and LCD shuttering
A technology dubbed dynamic backlighting is applied on modern LCD TVs to improve the contrast ratio
The shutters produce a contrast ratio of 1000:1 and dynamic backlighting enhances this ratio up to 30000:1
Power Consumption Prediction Function
Input of the function is the multimedia content
Output is power usage prediction

11. V. TV/FILM DETECTION Power Consumption Prediction Function Cont…..
Step one measures the power consumption for a series of pictures with the use of additive RGB color notation
bmin is the minimum brightness value that maximizes TV power consumption
A typical bmin value for the tested LCD TVs lies in the range {26, , 58}.
Step two extract the frames from a movie and determines the brightness of each frame

12. V. TV/FILM DETECTION Power Consumption Prediction Function Cont…….
Test for determining the value bmin

13. V. TV/FILM DETECTION
Power Consumption Prediction Function Cont….

14. V. TV/FILM DETECTION Preliminary Analysis
Extracted first 5 minutes of three movie files
Compared the actual power consumption against
values produced by the prediction function
Calculated the Pearson product moment
correlation coefficients between the actual
and predicted power consumption data.
The correlation for the three movie events
are 0.94, 0.98 and 0.93 respectively

15. V. TV/FILM DETECTION
Preliminary Analysis Cont…….

16. V. TV/FILM DETECTION Corridor Algorithm
To eliminate possible false matches
To make movie load signature
more distinguishable
The parameters for the decision are
threshold, corridor heights

17. V. TV/FILM DETECTION Automatic Detection of bmin
To identify a broad range of video material
A script to detect the optimal bmin values
Correlation between actual consumption
and power prediction is calculated for every
bmin value

18. V. TV/FILM DETECTION
Work-flow

19. V. TV/FILM DETECTION Other Television Models
Test results relating to 3 movies and 1 TV for other TV models
Movie/TV content identification via fine-grained smart meter data is possible

20. V. TV/FILM DETECTION Determining the Optimal Values
A series of test runs for varying parameters are
performed
No of false positive and correct identification
are recorded
0.85, 50%, 5% are the best identification rate
CONTENT IDENTIFICATION OF 12 MOVIE CHUNKS WITH DIFFERENT PARAMETERS

21. V. TV/FILM DETECTION False Positives with Other Appliances
To get some consolidated findings regarding false
content identification using workflow
Search of content in several 24h periods
Used 653 content files split into 5 min chunks
Considered correlation >=0.85
Identified 35.5 hits per 24hours
Log file clipping showing false positive matches on other appliances

22. V. TV/FILM DETECTION False Positives with Other Appliances
Sample example of a false positive match
The power curve does obviously not reflect
television operation.
The power consumption difference of more than
4000 watts is too high for being generated by TV set
The curve shape does not obey the shape
of prediction

23. VI. CONCLUSION
Transmission of unsigned and unencrypted data is a major throwback in data integrity and consumer privacy
At least a major privacy issue regarding content identification via a smart meter
Research has shown that identification of appliance’s behavior, television program and TV watching habits can be done by analyzing the smart meter power consumption data and electricity usage profile .
Smart meter data protection standards need to be defined and fulfilled, before a meter becomes fully operational and capable of preserving a user privacy

24. REFERENCES
[1] The basics of tv power. April
[2] Researchers analyze smart meter data September 2011.
[3] A. Rial and G. Danezis and M. Kohlweiss. Differential private billing with rebates. Technical Report MSR-TR , Microsoft Research, Feburary 2011.
[4] Gergely Acs and Claude Castelluccia. Dream: Differentially private ´ smart metering. CoRR, abs/ , 2012.
[5] G.Danezis A.Rial. Privacy-Preserving Smart Metering, MSR-TR
[6] Miro Enev, Sidhant Gupta, Tadayoshi Kohno, and Shwetak N. Patel. Televisions, video privacy, and powerline electromagnetic interference. In ACM Conference on Computer and Communications Security, pages 537–550, 2011.

25. QUESTIONS??