MAIN ISSUES WITH

VIDEO DATA ANONYMIZATION AND

FEATURE EXTRACTION?

Göteborg, 31 August, 2016

Helena Gellerman, SAFER

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CONTENT

• Personal Data

• New European data privacy law 2018

• Protecting data privacy

• Anonymization

• Feature extraction

• Presentations during the workshop

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TeleFOT

Safety

Pilot

PERSONAL DATA • Continuous video

– Internal on driver/passengers

– External on other road users

• Continuous GPS positions from start to end of trip

• Large datasets have been collected

• Global level – US, EU, Japan, Australia, Korea, China

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UDRIVE

DriveC2X

euroFOT 100 car

SmartWay

SHRP2

IVSBSS

Automation

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DATA PRIVACY NEW EUROPEAN LAW BY MAY 25TH, 2018

• Now: different national laws with

additions to EC Directive

• 2018: Same law across Europe

• EC - Data protection group -help

interpretation;

• Right to delete data

• Pseudo-nomisation, likely recognisable

• Legal/cultural differences => file data

privacy case in other country

• Fine up to 4% of gross profit if violation

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Directive 95/46/EC

§

National law on data

privacy

European law

§

European

law on data

privacy

Europe EC Country

Now

2018

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PROTECTING DATA PRIVACY

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Secure enclave Remote desktop with access control Trust between partners

Back-office - Consent;

anonymization if sharing

Anonymization by design - Consent not

possible - Privacy law not

applicable

List of features/ Code books Manual annotations Automated extraction

Data protection Anonymization Feature extraction

Data Video

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MAIN ISSUES - ANONYMISATION

Keep the richness of the original data

• Ways of preserving privacy

– Extraction of information – original data not

shared

– Anonymisation of original data while keeping

essential information

• Identify essential data for different areas of use

• Harmonization of extracted features

• Identify which data for future use to be extracted

before deletion

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MAIN ISSUE - WHAT TO PRESERVE

• Internal video

– Emotions of the driver

– Head and eye movements

– Body movements and tasks

• External video

– Traffic scenarios

– Detailed information on the interaction with

other traffic participants

• GPS

– Keep the start and end of trips as far as

possible

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ANONYMIZATION OF VIDEO DATA

6 DIFFERENT APPROACHES

Video anonymization • Blurring/Black spot

• Pixelation of picture

• Bar mask over eyes

• Negative of photo

• Mask identity – avatar face with same expressions

• Masked face - characteristics of another face is used to transform the face

Workshop 2015

• Swedish consortium and SRI International using avatars

• Carnegie Mellon using Masked Face

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EXAMPLE OF ANONYMIZATION - AVATAR CHALMERS, VOLVO, SMARTEYE, RÄVEN

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FEATURE EXTRACTION FROM VIDEO

TODAY

• Manual annotation tools

• Finding candidates

• Manual coding

– time consuming

– Costly

– Subjective

• Code book limitations

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FEATURE EXTRACTION FROM VIDEO

MANUAL => AUTOMATED

Automated extraction using machine learning / deep

learning algorithms

Main issues:

• Validation – detect the right things

• Large manuallly annotated dataset for validation/training

• Uncontrolled environment – Low resolution

– People moving out of scope

– Time of day, weather

– Differences in camera mounting, glasses, driving position and drivers’

length

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WORKSHOP PRESENTATIONS

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SNIC SENS – secure data processing of sensitive personal data

US perspective Differential privacy for data mining and querying Video anonymization of vehicle environment

Feature extraction permitting deletion of data Automated video annotation Automated labelling and recognition

Data protection Anonymization Feature extraction

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QUESTIONS TO DISCUSS

• When is an image anonymized/from what level can a

person be re-identified?

• Enough with face and license plates?

• Skip masked faces and use a black spot with

”expression signals” with the image?

• Different needs between real-time decision and research

analysis of images?

• How to achieve validated feature extraction algorithms?

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