This document discusses sensitivity analysis of smart meter data for privacy negotiation in IoT applications. It proposes an algorithm to detect sensitive points in smart meter data using kurtosis, Hampel identifier, and modified Rosner filter. The algorithm computes a sensitivity density to quantify privacy. Results on a public dataset show the algorithm detects sensitivity with high accuracy while preserving privacy of appliances like fridges. Future work aims to reduce complexity and improve privacy quantification when analyzing collective energy usage patterns across households.

1. 1Copyright © 2011 Tata Consultancy Services
Limited
Sensor Data Sensitivity Analysis for Privacy
Negotiation in IoT with focus on Smart Meters
Dr. Arpan Pal
Principal Scientist and Research Head
Innovation Lab, Kolkata
Tata Consultancy Services
With Arijit Ukil and Soma Bandyopadhyay, Innovation Lab, Kolkata

2. Motivation
Problem Statement
Proposed Approach and Results
Conclusion and Future Work

3. 3
Signal
Processing
Internet-of-Things - towards Intelligent Infrastructure
Sense
Extract
Analyze
Respond
Learn
Monitor
Intelligent
Infra
@Home
@Building
@Vehicle
@Utility
@Mobile
@Store
@Road
“Intelligent” (Cyber) “Infrastructure” (Physical)
APPLICATION SERVICES
BACK-END PLATFORM
INTERNET
GATEWAY
Sense
Extract
Analyze
Respond
Communication
Computing

4. 4
Home Utility Management Solution (HUMS)
Utility
Appliances Smart
Plugs
Intelligent
Gateway
Smart
Meter
Demand Forecasting
Demand Response
Appliance Management
Consumption View
Appliance Scheduling
On-off Control
Social Network
Integration
Consumer Home
Analytics
 Numerous data are going out
 Carries sensitive / private information.
 Disaggregation of per appliance based consumption.
 Better utility: more data granularity: more private info.
 Customer @ Netherlands
 Need to know Appliance Type / make / Model from
aggregated Smart Meter Data
 Disaggregation based NILM

5. 5
Smart Energy Disaggregation: Privacy Concern
 Activity monitoring
 Advantage: Personalized services and recommendation like theft detection,
elderly monitoring (University of Virginia’s ALARMNET, Harvard’s CodeBlue)
 Privacy issue: Leads to private data (smart meter data) leakage, Activity at Home
becomes known – can lead to various privacy leakage –especially for consumption
behaviours that are anomalous w.r.t aggregate behaviour (intra-day, intra-week,
intra-month, intra-customer)
[1] [www.winlab.rutgers.edu/~gruteser/papers/fp023-roufPS.pdf
[1]

6. 6
Privacy Negotiation – Need for Sensitivity Analysis
• Helps Users to get a better understanding of privacy leakage from the
data they are sharing – is it worth the utility provided by the application?
• Help in comparing similar utility applications from privacy perspective
• Tune Privacy preservation adaptively before sharing data to applications
Privacy
Utility
Privacy
Preservation
Tool
Sensitivity
Analysis

7. Motivation
Problem Statement
Proposed Approach and Results
Conclusion and Future Work

8. 8
Sensitivity Detection and Analysis
 Privacy as a function of sensitivity detection and analysis
 Explores intrinsic statistical properties of smart meter data
(time-series data) for sensitivity analysis and detection
 Information theoretic model for privacy measurement
 Works on generic time-series data

9. 9
Privacy Analyzer in Smart Energy Management
PPDM: Privacy preserving data mining,
one of privacy enhancing methods [2]
× Only Anonymization does not help,
need to analyze the diversity also [3]
× Existing Diversity algorithms like l-
diversity are computationally heavy –
cannot suite IoT real-time requirement
× Existing diversity algorithms can work
on quasi-identifiers in sensor
metadata and not on the real sensor
data
 Unless we work on the sensor data, we
cannot get fine-grain privacy
negotiation control – need sensitivity
analysis
[2] M. Hamblen. “Privacy algorithms: Technology-based protections could make personal data impersonal”. Computerworld, Oct. 14
2002.
[3] Ashwin Machanavajjhala et. al., “ℓ-Diversity: Privacy Beyond k-Anonymity”, www.cs.cornell.edu/~vmuthu/research/ldiversity.pdf

10. 10
State of the Art – Sensitivity Analysis of Smart Meter Data
Intrusive monitoring
 Application-specific monitoring through dedicated hardware: using The Energy
detective (TED) [4], battery-based load obfuscation [5].
× Need Dedicated Hardware
× Need Training – Supervised Learning
Non-intrusive monitoring
× Supervised NILM using disaggregation [8]
Our Goal
 Improvement in NILM based Sensitivity Analysis for Privacy Negotiation as
compared to available privacy measures
× Z-score [6]: 3-sigma based analysis – very high false negatives
× Modified z-score [7]: high false negatives
[4] A. Molina-Markham, P. Shenoy, K. Fu, E. Cecchet, D. Irwin, "Private memoirs of a smart meter," ACM BuildSys, pp. 61 - 66, 2010.
[5] W. Yang, N. Li, Y. Qi, Wahbeh Qardaji, Stephen McLaughlin, Patrick McDaniel," Minimizing Private Data Disclosures in the Smart
Grid," ACM CCS, pp. 415-427, 2012.
[6] R. Rao, S. Akella, G. Guley, "Power Line Carrier (PLC) Signal Analysis of Smart Meters for Outlier Detection," IEEE SmartGridComm,
pp. 291 - 296, 2011.
[7] R. M. Nascimento, et al., Outliers’ Detection and Filling Algorithms for Smart Metering Centers ," IEEE PES , pp.1 - 6, 2012.
[8] K. Srinivasarengan, Y.G. Goutam, M.G. Chandra, and S. Kadhe, "A Framework for Non Intrusive Load Monitoring Using Bayesian
Inference," IEEE IMIS, pp. 427 423, 2013.

11. Motivation
Problem
Proposed Approach and Results
Conclusion and Future Work

12. 12
Proposed Sensitivity Detection Algorithm
1. Find kurtosis to understand spread of distribution of meter data
2. If kurtosis < 3: use Hampel [9] identifier for detecting sensitivity points
 Minimizes masking effect through outlier processing – reduces false negatives
3. Else: use modified Rosner [10] filter for sensitivity detection
 Minimizes swamping effect through iterative backward testing - reduces false
positives
 Modification in backward testing criteria through fitting in student-t
distribution
4. Compute Sensitivity Density by normalizing within given time period under
consideration
5. Privacy Negotiation using the Sensitivity Density
 Additional application specific parameter tuning – sampling resolution, block
size
[9] Hampel: H. Liu, S. Shah, W. Jiang, "On-line outlier detection and data cleaning," Elsevier Computers and Chemical
Engineering, pp. 1635–1647, 2004.
[10] Rosner: B. Rosner, “On the Detection of Many Outliers.” Technometrics, vol. 17, pp. 221–227, 1975.

13. 13
Sensitivity Detection - Results
[11] Experimented with REDD dataset (Z. Kolter, and M, J. Johnson, "REDD: A public data set for energy disaggregation
research," SustKDD, 2011) of 24 hour, 1 Hz sampling
Proposed Scheme Proposed Scheme

14. 14
Privacy Gain Analysis: Results
Privacy gain w.r.t. NILM [8]
Privacy gain w.r.t. supervised learning [4]
Proposed Scheme
Proposed Scheme

15. 15
Privacy Breach Attack Analysis
Privacy
Quantification
Sensitivity
detection
Privacy
Preservation
Privacy Breach Attack
with Disaggregation
Algorithm (NILM)
Smart meter
data
Privacy Preserved
Data Out
Privacy Breach
Attack Success
Count

16. 16
Privacy Breach Attack: Results
Privacy Analyzer successfully
defends leaking of fridge and
high power appliance signature.
[12] Submitted in Infocom 2014, IPSN 2014
[13] Two patent filing under progress

17. Motivation
Problem
Proposed Approach and Results
Conclusion and Future Work

18. 18
Conclusion
 Sensitivity detection with higher accuracy: minimal false positive
and negative alarms.
 Privacy-utility trade-off with low power appliance detection
capability while fridge and high power appliance detection
suppressed.
 Cost-effective compared to supervised learning based schemes,
which require additional hardware like TED (The Energy
Detector). Assuming a household has around 10 electrical
appliances; using TED 5000-C costs more than 2000 USD.
 No additional infrastructure, non-invasive: high scalability,
maintainability.

19. 19
Future Work
 Further Reduce Computational Complexity
 Improve Privacy Quantification Calculation
 Sensitivity analysis through learning of collective energy usage
pattern: Detection of sensitivity considering large set of smart
meter data from number of households.
– Detection of Unusual Pattern across days / months / seasons /
consumers

20. Thank You
arpan.pal@tcs.com