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Tools for Privacy-Preserving Data Analysis
Adria Gascon
agascon@turing.ac.uk
Alan Turing Institute Warwick University
Privacy-Enhancing
Technologies
● Multi-party computation
● Homomorphic encryption
● Differential privacy
● Secure enclaves...
Privacy is Important
For ethical reasons
For regulatory reasons
For economic reasons
Privacy is Important
For ethical reasons
For regulatory reasons
For economic reasons
Government and industry seek
practica...
Privacy is Important
For ethical reasons
For regulatory reasons
For economic reasons
Government and industry seek
practica...
Privacy is Important
Government and industry seek practical and usable privacy technologies
but this is a tough task...Why...
Data privacy is a slippery concept
How much does an analysis reveal?
How much does an analysis reveal?
About sensitive attributes
in the dataset?
How much does an analysis reveal?
About whether someone
is in the dataset?About sensitive attributes
in the dataset?
How much does an analysis reveal?
About whether someone
is in the dataset?About sensitive attributes
in the dataset?
About...
How much does an analysis reveal?
About whether someone
is in the dataset?About sensitive attributes
in the dataset?
About...
Telling apart...
1. The ethical from the unethical
2. The legal from the illegal
3. The possible from the impossible
a) Th...
Telling apart…
possible from impossible
Talking privately (warmup)
Talking privately (warmup)
Hello!
Let’s gossip!
Talking privately (warmup)
Curve25519, …?
Sure
Talking privately (warmup)
&%$$$*~~`>¬@<
£(*^£##lkjd£(
“)&”£^^Lk”0hd_
322:<**__£(
Computing on secrets
Computing on secrets
Computing on Secrets
...
Computing on Secrets
...
How do we get rid of the trusted party?
Is this even possible?
Secure Multi-Party Computation
...
How do we get rid of the trusted party?
Is this even possible?
Secure Multi-Party Computation
...
How do we get rid of the trusted party?
Is this even possible?
POSSIBLE
Collaborative analyses
...
Secure Distributed Training
• Two or more parties want to jointly learn a model of their data
•...
Answering embarrassing questions
Yes/No
Do you like pineapple…
Do you like pineapple…
...in your pizza?
Answering embarrassing questions
Can we collect group statistics
while preserving individual privacy?
Answering embarrassing questions
Can we collect group statistics
while preserving individual privacy?
POSSIBLE
Randomised response
1.Flip a coin
2.If tails, then respond truthfully
3.If heads, then flip a second coin and
a)If heads r...
● Protect data in storage
● Protect data in transit
● Protect data while disclosing it
● Protect data while computing on i...
● Protect data in storage
● Protect data in transit
● Protect data while disclosing it
● Protect data while computing on i...
● Protect data in storage
● Protect data in transit
● Protect data while disclosing it
● Protect data while computing on i...
● Protect data in storage
● Protect data in transit
● Protect data while disclosing it
● Protect data while computing on i...
Conclusion
● Privacy-preserving data analysis enables new applications.
● Practical Secure computation and data analysis a...
Thanks!
In:Confidence 2019 - Tools for privacy-aware data analysis
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In:Confidence 2019 - Tools for privacy-aware data analysis

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Dr Adrià Gascón, Research Fellow at the Alan Turing Institute talks about the main tools for privacy-aware data analysis on the In:Confidence 2019 main stage (April 4th at Printworks, London).

Published in: Data & Analytics
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In:Confidence 2019 - Tools for privacy-aware data analysis

  1. 1. Tools for Privacy-Preserving Data Analysis Adria Gascon agascon@turing.ac.uk Alan Turing Institute Warwick University
  2. 2. Privacy-Enhancing Technologies ● Multi-party computation ● Homomorphic encryption ● Differential privacy ● Secure enclaves ● ... Data Analysis ● Machine learning ● Statistics ● Databases ● ... Privacy-Aware Data Analysis
  3. 3. Privacy is Important For ethical reasons For regulatory reasons For economic reasons
  4. 4. Privacy is Important For ethical reasons For regulatory reasons For economic reasons Government and industry seek practical and usable privacy-friendly data analysis technologies
  5. 5. Privacy is Important For ethical reasons For regulatory reasons For economic reasons Government and industry seek practical and usable privacy-friendly data analysis technologies but this is a tough task...Why?
  6. 6. Privacy is Important Government and industry seek practical and usable privacy technologies but this is a tough task...Why? Technology is not quite there? Regulation is not quite there? Incentive systems and not well aligned? All of the above?
  7. 7. Data privacy is a slippery concept
  8. 8. How much does an analysis reveal?
  9. 9. How much does an analysis reveal? About sensitive attributes in the dataset?
  10. 10. How much does an analysis reveal? About whether someone is in the dataset?About sensitive attributes in the dataset?
  11. 11. How much does an analysis reveal? About whether someone is in the dataset?About sensitive attributes in the dataset? About the whole population?
  12. 12. How much does an analysis reveal? About whether someone is in the dataset?About sensitive attributes in the dataset? About the whole population? To whom? How powerful is the attacker? Only the output? Or also intermediate values?
  13. 13. Telling apart... 1. The ethical from the unethical 2. The legal from the illegal 3. The possible from the impossible a) The practical from the impractical
  14. 14. Telling apart… possible from impossible
  15. 15. Talking privately (warmup)
  16. 16. Talking privately (warmup) Hello! Let’s gossip!
  17. 17. Talking privately (warmup) Curve25519, …? Sure
  18. 18. Talking privately (warmup) &%$$$*~~`>¬@< £(*^£##lkjd£( “)&”£^^Lk”0hd_ 322:<**__£(
  19. 19. Computing on secrets
  20. 20. Computing on secrets
  21. 21. Computing on Secrets ...
  22. 22. Computing on Secrets ... How do we get rid of the trusted party? Is this even possible?
  23. 23. Secure Multi-Party Computation ... How do we get rid of the trusted party? Is this even possible?
  24. 24. Secure Multi-Party Computation ... How do we get rid of the trusted party? Is this even possible? POSSIBLE
  25. 25. Collaborative analyses ... Secure Distributed Training • Two or more parties want to jointly learn a model of their data • But they can’t share their private data with other parties Secure Distributed Prediction • A server holds a private model f, a client holds private data x, and the client wants to obtain f(x) • But they can’t share f or x with other parties
  26. 26. Answering embarrassing questions Yes/No
  27. 27. Do you like pineapple…
  28. 28. Do you like pineapple… ...in your pizza?
  29. 29. Answering embarrassing questions Can we collect group statistics while preserving individual privacy?
  30. 30. Answering embarrassing questions Can we collect group statistics while preserving individual privacy? POSSIBLE
  31. 31. Randomised response 1.Flip a coin 2.If tails, then respond truthfully 3.If heads, then flip a second coin and a)If heads respond “yes” b)If tails respond “no”
  32. 32. ● Protect data in storage ● Protect data in transit ● Protect data while disclosing it ● Protect data while computing on it Privacy comes in many forms
  33. 33. ● Protect data in storage ● Protect data in transit ● Protect data while disclosing it ● Protect data while computing on it Privacy comes in many forms Differential privacy
  34. 34. ● Protect data in storage ● Protect data in transit ● Protect data while disclosing it ● Protect data while computing on it Privacy comes in many forms Homomorphic encryption
  35. 35. ● Protect data in storage ● Protect data in transit ● Protect data while disclosing it ● Protect data while computing on it Privacy comes in many forms Multi-Party Computation
  36. 36. Conclusion ● Privacy-preserving data analysis enables new applications. ● Practical Secure computation and data analysis are developing a simbiotic relationship. ● A fully-fledged system need to consider several notions of privacy, and their combination needs to be understood and exploited.
  37. 37. Thanks!

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