Consent , crypto and information infrastructure systems

Consent , crypto and information infrastructure systems

• 1.
  Consent, Crypto and Information Infrastructures
• 2.
  What this talkis about? Introduction to Digital Infrastructure. (5 Minutes) Use of Crypto in Digital Infrastructures (20 Minutes). Relationship between Crypto and Consent. (15 Minutes)
• 3.
  Introduction to Digital Infrastructure
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  Physical Infrastructure
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  Financial Infrastructure
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  Digital Infrastructure  Infrastructureas a Service (IaaS)  Software as a Service (SaaS)  Platform as a Service (PaaS)
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  “ ” Digital Infrastructures are Informationprocessing Infrastructures.
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  Atomic Operations inData-Verse Store AnalyseCreate
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  Use of Crypto inDigital Infrastructures
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  First came theData Storage
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  Then came theProcessing
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  And then youbring your data
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  Atomic Operations inData-Verse Store AnalyseCreate S3EBS EC2 Compute
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  AWS Revenues Year RevenueLoss 2006 $20M $13M 2008 $60M $8M
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  Bring Your OwnKeys *5 Years later after S3
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  EBS Encryption *6 Yearslater after EBS Announcement
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  Full Fledged KeyManagement
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  Bring your ownKeys
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  Atomic Operations inData-Verse Crypto Analyse StoreCreate
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  Why is Cryptoimportant? Whoever controls the data is the owner of the data. 1 Where data is stored is irrelevant if control is yours. 2 Unlike “physical things, in information infrastructures, control, storage and ownership are “different things” 3
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  Dis-trust is anEmergent Property Distrust Control StorageOwnership
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  Crypto Can mediateTrust CRYPTO Control StorageOwnership
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  Crypto Key management used tobe hard problem
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  But not anymore
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  Everyone manages theirown Keys
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  And 100s of themevery single day
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  OSS and InformationInfra-Stacks  Crypto Protocols are OSS.  Storage Protocols are OSS. (S3, Block Storage, NFS, Ceph, Gluster FS, ZFS, LVM)  Storage Services (DynamoDB, Cassandra etc.)  So how do Digital infrastructures make money? – Efficiency, Optionality and Trust (provided by Crypto)
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  Public Locker
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  But I wantto ”Share Information” Sharing Information means you “lose control” of it technically. The fiction of consent is then created by Legal principles and through policy. Multiple schemes exist that allow Limited sharing but with Bring your own Cryptography algorithms and Bring your own keys approach.
• 30.
  Property Preserving Encryption  IfPlain Text (A) < Plain Text (B), then  Encrypted (A) < Encrypted (B).  If Plain Text(A) OPERATOR Plain Text (B)  Encrypted (A) OPERATOR Encrypted (B)  Leaks some information and is vulnerable to repeated queries.  Combined with transformation to apply anonymization offers better protection
• 31.
  PPE Applicability  ADocument (X) can be thought of to have “N” Fields  Secret ID  Name  Date of Birth  Address  Different PPE algorithms can be applied to different fields based on what the “Sharer” wants to.  Secret ID (Full encryption)  Name (No Encryption)  Date of Birth (PPE that transforms into Age number and then applies algorithm that preserves < and > operations)  Address (Encrypt everything)
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  What PPE offers Informational self- determination. Ishare not just fields but can also determine the PPE on some fields. Composable with Other Anonymization functions – PPE(T(X)) Allows T(X) to be built for fields which can be normalized to PPE fields
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  Extraction attacks onPPE
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  Extraction attacks onPPE
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  Differential Privacy  StrongMathematical foundation that allows balancing utility and privacy and guarantees  No access to raw data.  Resilience to post-processing.  Resilience to de-anonymization techniques via constant querying.  Allows aggregation but evades statistical inference.
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  ε-differential privacy  Aninference is released from a statistical database and it should not compromise the individual’s privacy.  A perfect privacy score would only be possible when the individual’s data is not in the DB.  Goal of Differential Privacy = Roughly same privacy to individual as if their data is not in the DB.
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  Multiple successful real-world cases Windows Telemetry Linked-in Advertiserqueries US Census for commuting patterns.
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  Case Study: USCensus Bureau
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  Privacy Pie Crypto Key Management Storage Management PrivacyServices Utility Layers
• 41.
  Why is Consent broken?
• 42.
  How not to break Consent?