Showing posts with label Privacy. Show all posts
Showing posts with label Privacy. Show all posts

2019-02-13

2019-02-13 Wednesday - Homomorphic Encryption

An interesting discussion arose in the last week, which introduced me to the concepts of using Homomorphic Encryption in machine learning solutions.



https://en.wikipedia.org/wiki/Homomorphic_encryption


IBM's Blindfolded Calculator 

A very casual introduction to Fully Homomorphic Encryption


Encrypt your Machine Learning
How Practical is Homomorphic Encryption for Machine Learning?



A FULLY HOMOMORPHIC ENCRYPTION SCHEME 
  • A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
  • Craig Gentry, September 2009


A brief survey of Fully Homomorphic Encryption, computing on encrypted data


Homomorphic EncryptionShai Halevi (IBM Research)April 2017
"Fully  homomorphic  encryption  (FHE)  has  been  called  the  “Swiss  Army  knife  of  cryptog-raphy”,  since  it  provides  a  single  tool  that  can  be  uniformly  applied  to  many  cryptographicapplications.  In this tutorial we study FHE and describe its different properties, relations withother concepts in cryptography, and constructions.  We briefly discuss the three generations ofFHE constructions since Gentry’s breakthrough result in 2009,  and cover in detail the third-generation scheme of Gentry, Sahai, and Waters (GSW)"

Fully Homomorphic Encryption: Cryptography’s Holy Grail 

Cryptology ePrint Archive: Report 2015/1192
A Guide to Fully Homomorphic Encryption

2018-08-06

2018-08-06 Monday - GDPR and Blockchain Privacy Implications

Some preliminary reading I'm doing...

GDPR



Blockchain Technology and the GDPR - How to Reconcile Privacy and Distributed Ledgers


Blockchains and Data Protection in the European Union

  • Max Planck Institute for Innovation & Competition Research Paper No. 18-01
    • https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3080322
    • "This paper examines data protection on blockchains and other forms of distributed ledger technology (‘DLT’). Transactional data stored on a blockchain, whether in plain text, encrypted form or after having undergone a hashing process, constitutes personal data for the purposes of the GDPR. Public keys equally qualify as personal data as a matter of EU data protection law. We examine the consequences flowing from that state of affairs and suggest that in interpreting the GDPR with respect to blockchains, fundamental rights protection and the promotion of innovation, two normative objectives of the European legal order, must be reconciled. This is even more so given that, where designed appropriately, distributed ledgers have the potential to further the GDPR’s objective of data sovereignty. "

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