This document summarizes privacy issues related to smart living technologies and proposes approaches to address them. It discusses how data collected from smart energy meters, occupancy detection sensors, and other IoT devices can reveal private information about users if not properly addressed. It proposes a hybrid information-centric approach using techniques like k-anonymity and differential privacy to selectively mask or obfuscate sensitive data while maintaining utility. This would involve statistical processing to identify outliers and quantify privacy and utility to allow adaptive techniques like differential privacy with variable sampling or obfuscation. Some initial results on these approaches have been published in conferences. Overall, the document outlines privacy risks with smart home technologies and proposes information-theoretic methods to balance privacy and utility of collected data

1. Privacy Issues in Smart Living
Dr. Arpan Pal
Principal Scientist and Research Head
Innovation Lab, Kolkata
Tata Consultancy Services Ltd.
IEEE Sr. Member
Associate Editor, IEEE and ACM Transactions
B.Tech, M.Tech and Ph.D. in Electronics and Telecommunication
January 24, 2014

2. The Holy Grail of Privacy
Data that is both contextually useful as well as forever privacy preserving
• Privacy agreements are ok for legalities sake – but does the average user
understand it?
https://www.privacyrights.org/fs/fs2b-cellprivacy.htm
• Main issue – Is the data I am giving out is worth the Utility I am getting?
PrivacyUtility

3. Smart Energy Meters – Utility
 Accurate billing
 Tailored energy efficiency advice – based on accurate data
specific to your home
 Understand how much appliances are costing you and check
if things are working properly
 More control over how much energy you’re using
 Efficient peak-load management and Demand-response
http://www.efoodsdirect.com

4. Source: www.winlab.rutgers.edu/~gruteser/papers/fp023-roufPS.pdf
Smart Energy Meters – Privacy Issues
 Could indicate your pattern of living and what you are doing
in your own home
 Bad guy knows when you're not at home and burgles your
house, or worse, he knows when only one old woman is at
home and breaks in
 User Profiling

5. Occupancy / Footfall Detection – Utility
Energy consumption and carbon footprint can be reduced by
using energy judiciously
• Occupancy and footfall give important feedback parameters for
energy management in public places
• When person is at Home, room-level occupancy can be used to
reduce energy footprint via automatic on-off switches
• At Office, zone/area level occupancy and presence can provide cues
to energy management like lighting, heating and cooling
• In hospitals, zone level occupancy can provide input for running
cooling / heating equipment.
http://www.lumenergize.com/
http://pixgood.com/
http://liveinnovations.com.auhttp://www.commlab.unimo.it

6. Occupancy Detection – Privacy Issues
 Home occupancy data can reveal pattern of living / activity /
absence at Home
 Location data at Malls can reveal shopping behavior pattern
 Recent MIT study showed that 4 spatio-temporal points,
approximate places and times, are enough to uniquely
identify 95% of 1.5M people in a mobility database
De Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel (March 25, 2013). "Unique in the Crowd:
The privacy bounds of human mobility". Nature srep.
Palmer, Jason (March 25, 2013). "Mobile location data 'present anonymity risk'". BBC News
http://www.etihad.com

7. Even Sleeping Smartphones Could Soon Hear Spoken Commands
Nuance is working with chipmakers on technology that would enable
“persistent listening” apps.
http://www.technologyreview.com/news/429316/even-sleeping-smartphones-could-soon-hear-spoken-
commands/
MIT Technology Review, Sept. 2012
Smartphone Malware Designed to Steal Your Life
The US Naval Surface Warfare Center has created
an Android app that secretly records your
environment and reconstructs it as a 3D virtual model
http://www.technologyreview.com/view/429394/placeraider-the-military-
smartphone-malware-designed-to-steal-your-life/
MIT Technology Review, Sept. 2012
Vehicle Trip Overlay Over a Year reveals your
hub locations (home, office??)
Source: https://www.aclu.org/technology-and-liberty/meet-jack-or-what-
government-could-do-all-location-data
Privacy Breach in other IoT Applications

8. Source: http://techcrunch.com/2014/11/13/u-s-authorities-are-reportedly-gathering-phone-data-using-fake-celltowers-on-planes/
What happens if this gathered data is leaked?
Big Brother Watching

9. Information-centric Approach
 Need something more than
anonymization and on-off control
 Hybrid approach using K-
Anonymity and Differential
Privacy – selective masking /
obfuscation of data
 Information Theoretic Smart
Privacy Analyzer (SPA)
 Statistical Processing based
Outlier Detection to identify
Sensitive data
 Information-theoretic privacy
measure
 Adaptation for differential
privacy possible using
variable sampling rate or
obfuscation or randomization
Arijit Ukil et. al., BuildSys 2014 Demo

10. Some Results
Publications in Infocomm, Buildys and ICC

11. Points to Ponder
• How to quantify utility and privacy
• Privacy control at user hand
Thank You
arpan.pal@tcs.com