Privacy Tools and Techniques for Developers -Amber Welch - - PowerPoint PPT Presentation

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Privacy Tools and Techniques for Developers -Amber Welch - - PowerPoint PPT Presentation

Oct 01, 2023 208 likes •781 views

Privacy Tools and Techniques for Developers -Amber Welch bit.ly/2x1UXWX Amber Welch MA, CISSP, CISA, CIPP/E, CIPM, FIP, CCSK, and ISO 27001 Lead Auditor linkedin.com/in/amberwelch1 github.com/msamberwelch @MsAmberWelch bit.ly/2WRAGh8

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Privacy Tools and Techniques for Developers

  • Amber Welch

bit.ly/2x1UXWX

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Amber Welch

MA, CISSP, CISA, CIPP/E, CIPM, FIP, CCSK, and ISO 27001 Lead Auditor linkedin.com/in/amberwelch1 github.com/msamberwelch @MsAmberWelch

bit.ly/2WRAGh8

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  • Privacy Engineering Intro
  • Privacy by Design
  • Privacy Enhancing Technologies

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First, an apology.

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SLIDE 5

Legal teams have often kept tech out of privacy.

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Developers don’t know privacy concepts. Privacy teams haven’t taught them.

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SLIDE 7

Privacy Impact Assessment

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Description

A Privacy Impact Assessment (PIA) is a method to:

  • Identify privacy risk
  • Map personal data flows
  • Document privacy risk mitigations
  • Fulfill regulatory requirements

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SLIDE 9

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Use Cases

  • New applications
  • Adding functions and features
  • Collecting new sensitive personal data
  • Annual reviews or audits
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Tasting Notes

Benefits

  • Legal compliance
  • Identify and reduce

privacy risks

  • Catch privacy errors

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Tasting Notes

Benefits

  • Legal compliance
  • Identify and reduce

privacy risks

  • Catch privacy errors

Limitations

  • High time investment
  • Ineffective if not

completed well

  • Not a security risk

assessment

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SLIDE 13

Data Minimization and Retention

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Description

Data minimization is:

  • Collecting only necessary data
  • Maintaining and updating data
  • Deleting old data that isn’t needed

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Use Cases

  • New applications
  • API integrations
  • Adding functions and features
  • Collecting new personal data
  • Customer termination
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Tasting Notes

Benefits

  • Legal compliance
  • Minimize volume of data

to be breached

  • Improve data quality

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Tasting Notes

Benefits

  • Legal compliance
  • Minimize volume of data

to be breached

  • Improve data quality

Limitations

  • Users may be frustrated
  • Companies like to keep

all the data

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SLIDE 18

Default Settings

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Description

Default settings for privacy should:

  • Minimize personal data collected
  • Prevent default data sharing
  • Require enabling of intrusive settings
  • Avoid making data public by default

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Less than 5% of general users change any default settings, while programmers change 40% of settings.

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SLIDE 21

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SLIDE 22

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Tasting Notes

Benefits

  • Reputation for privacy
  • Reduce user frustration
  • Protect less educated

users

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Tasting Notes

Benefits

  • Legal compliance
  • Reputation for privacy
  • Reduce user frustration
  • Protect less educated

users Limitations

  • Companies may want to

monetize intrusive apps

  • Requires privacy

awareness at design

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SLIDE 25

Encryption

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Encrypt these:

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  • TLS
  • Email and messaging
  • Databases
  • Cloud storage
  • Backups
  • Password management
  • Endpoint devices
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Don’t:

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  • Make your own crypto
  • Use deprecated crypto (i.e., SHA1)
  • Hard code keys
  • Store keys on the same server as the data
  • Use one key for everything
  • Skip password hash and salt
  • Forget to restore certificates after testing
  • Use old crypto libraries
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Differential Privacy

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Description

Differential privacy:

  • Adds statistical noise to a data set
  • Prevents identification of one

individual’s record

  • Provides the same results as the raw

data would, with or without one record

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SLIDE 30

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SLIDE 31

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SLIDE 32

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Tasting Notes

Benefits

  • Limit insider threats
  • Increase data usability
  • Allows for collaboration

without exposing data

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Tasting Notes

Benefits

  • Legal compliance
  • Limit exposure from

security incidents

  • Limit insider threats

Limitations

  • Works best on large

databases

  • Must be tuned well

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Privacy Preserving Ad Click Attribution

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Description

Privacy preserving ad click attribution:

  • Allows ad attribution monetization
  • Prevents user ad click tracking
  • Uses the browser to mediate ad clicks

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SLIDE 37

bit.ly/30FFBoj

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bit.ly/30FFBoj

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SLIDE 39

bit.ly/30FFBoj

Available now as an experimental feature

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Tasting Notes

Benefits

  • Allows websites to still

monetize content

  • Could become a W3C

web standard

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Tasting Notes

Benefits

  • Allows websites to still

monetize content

  • Could become a W3C

web standard Limitations

  • Needs widespread

adoption to be effective

  • Users may not believe

any ads respect privacy

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Federated Learning

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Description

Federated learning:

  • Trains a central model on

decentralized data

  • Never transmits device data
  • Sends iterative model updates to

devices which return new results

  • Uses secure aggregation to decrypt
  • nly the aggregate and no user data

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SLIDE 44

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SLIDE 45

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Use Cases

  • Android’s Gboard prediction model
  • Health diagnostics
  • Behavioral preference learning
  • Driver behavior
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Tasting Notes

Benefits

  • Speeds up modeling

and testing

  • Minimally intrusive
  • Individual data is not

accessible to the central model

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Tasting Notes

Benefits

  • Speeds up modeling

and testing

  • Minimally intrusive
  • Individual data is not

accessible to the central model Limitations

  • Errors could cause

private data leakage

  • Requires a large user

base

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Homomorphic Encryption

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Description

Homomorphic encryption:

  • Allows computation on ciphertext
  • Enables collaboration without

disclosing confidential data

  • Only the calculation results can be

decrypted

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SLIDE 51

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Use Cases

  • Computations on data shared across organizations
  • Research using highly sensitive records
  • Processing by employees with a lower clearance
  • Google’s open source Private Join and Compute
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Tasting Notes

Benefits

  • Reduces insider threat
  • Increases collaboration
  • Increases data usability

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Tasting Notes

Benefits

  • Reduces insider threat
  • Increases collaboration
  • Increases data usability

Limitations

  • Resource-intensive
  • Limited functions
  • No fully homomorphic

encryption available yet

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Becoming a Privacy Champion

bit.ly/2x1UXWX

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Amber Welch

MA, CISSP, CISA, CIPP/E, CIPM, FIP, CCSK, and ISO 27001 Lead Auditor

linkedin.com/in/amberwelch1 github.com/msamberwelch @MsAmberWelch

bit.ly/2WRAGh8