1 Copyright © 2011 Tata Consultancy Services Limited

Sensor Data Sensitivity Analysis for Privacy Negotiation in IoT with focus on Smart Meters

Dr. Arpan PalPrincipal Scientist and Research HeadInnovation Lab, Kolkata Tata Consultancy Services

With Arijit Ukil and Soma Bandyopadhyay, Innovation Lab, Kolkata

MotivationProblem StatementProposed Approach and ResultsConclusion and Future Work

3

SignalProcessin

g

Internet-of-Things - towards Intelligent Infrastructure

Sense

Extract

Analyze

RespondLearn

Monitor

IntelligentInfra

@Home@Building

@Vehicle@Utility

@Mobile

@Store

@Road“Intelligent” (Cyber) “Infrastructure” (Physical)

APPLICATION SERVICES

BACK-END PLATFORM

INTERNET

GATEWAY

Internet-of-Things (IoT) Framework

Sense

Extract

Analyze

Respond

Communication

Computing

4

Home Utility Management Solution (HUMS)

Utility

Appliances Smart Plugs

IntelligentGateway

Smart Meter

Demand ForecastingDemand ResponseAppliance Management

Consumption ViewAppliance SchedulingOn-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

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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]

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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

PrivacyUtility

Privacy Preservati

on Tool

Sensitivity Analysis

MotivationProblem StatementProposed Approach and ResultsConclusion and Future Work

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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

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Privacy Analyzer in Smart Energy ManagementPPDM: 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

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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.

MotivationProblemProposed Approach and ResultsConclusion and Future Work

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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.

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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

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Privacy Gain Analysis: Results

Privacy gain w.r.t. NILM [8]

Privacy gain w.r.t. supervised learning [4]

Proposed Scheme

Proposed Scheme

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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

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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

MotivationProblemProposed Approach and ResultsConclusion and Future Work

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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.

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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

Thank You

arpan.pal@tcs.com