g x, Yén 2y by RERI Key Event Receipt Infrastructure The Economics of Its & Bits Digital Identity Freedom Privacy Control Security
Economics value creation and capture ntrol A o M extraction, exchange, and exploitation value security
its control bits @ value ) |0||°" atoms « information 000 Wy : physical security Security? Informational security P B m Ry [e) o= Ao = N
Revenue 60 Years of Its & Bits Market Value 1960 1980 2000 2020 2020 e o g o o g e Sy e e e e 1 |@anaral Motors. M2 1 Eomae w0 1 Geesloos 08 1 Wemet smE 1 Mems 1w 2 looenwest | Ten 7 Gt w3 e @ 2 |eaman w2 s 3 [roaveie s 0w w3 e w2 o e 5w s [t 4 o oo TR o e 4 e s o fussos ot e Wm0 Gemer 05 osin E I s o oo . IR HS5 b ST s s 2 v [anor a1 e a7 G L T T T o b e PN s | b T @9 e e b e P 3 [ehmer 208 s e a0 Ao am o wE s e v o [esman s 0 e | 0 som T reserstege T 1 e e 1 a1 e R e N R T T 15 Joes s R N ) o man o [ e o w92 e W6 (s s 6 e T |t 1121 s W i G| 7 (et W 7 o s - [scn WO 0 Tstimie 08 e a6 et wE e ar 1 eoang e W s W0 |wmsonms wE 8 Gadh w ™ s @ s iR e e [ i e
Networks Effects Network scaling law: How network value scales with number of participants. TN Value = Reach (= ) XL > ‘k&" “"fi‘\ Metcalfe’s Law gy ‘;%4;&3’ V=a-N-N=ane LA “".’V https:/imedium.com/selfrule/metaplatforms-and-cooperative-network-of-networks-effects-6e61ebiscs86
How do we recapture the value in our data? 1- Retake control of our data 2- Leverage cooperative network effects
Retake control of our data
Human Basis-of-Trust “in person” I can know you - therefore | can trust you AR L;/J"; ;
LA B
, ] ‘ |
“on the internet”
I can’t really know you - therefore | can’t really trust you
A o B A o o R y | . . s . Toolkits e > e b F W 5 i e O i - - B e . e : A e | 5 § i E o = L ;4 (& | | | . i | = . L0y . . | | T it 4 = TN \‘i %‘fl/ “ o [ Uv“mufllmxfi
Only have one set of tools for truly secure data control!
its bits control @ value @ Ifllllo on atoms = information 000 dlgltal EQ} uniqueness ? physical security E n t rO py Informational security _fl A 8 S, B sl &
Entropy Derived Tools Cryptographic one-way functions ... hashes, ECC scalar multiplication...
digital signatures, ZKPs ...
Information uniqueness from captured entropy To retake control of our data we must first retake control of our identifiers. self-certifying pseudonymous identifiers Key Event Receipt Infrastructure (KERI) https://arxiv.org/abs/1907.02143
Replace human basis-of-trust with cryptographic root-of-trust. With verifiable digital signatures from asymmetric key crypto — we may not trust in “what” was said, but we may trust in “who” said it. We may verify that the controller of a private key, (the who), made a statement but not the validity of the statement itself. The root-of-trust is consistent attribution via verifiable integral non-repudiable statements We may build trust over time in what was said via histories of verifiably attributable (to whom) consistent statements i.e. reputation.
Four A’s of Secure Data Control Author: creator, source-of-truth Authentic: provable origin, root-of-trust Authorized: consent, loci-of-control Authoritative: accurate, reputable A4 data control securely established via self- certifying pseudonymous identifiers
Sapored Data
Sapor: noun
the quality in a substance that affects the sense of taste; savor; flavor. Sapored data may be securely provenanced to its author(s). Sapored data value extraction may be securely attributed to its authors. Sapored data supply chains. Enable consumer pull in addition to regulatory push. Conscious consumers of Sapored data drive compliance. Circular data economy network effects.
) 000 control l @ correlation bltS @ cryptographic 1o g |on::| pseudonymous identifiers contmll Tattribution @ sapored data
Strong Privacy? Weak Weak
Strong Privacy
un-correlated interactions over unbounded time and space.
Super aggregators and state actors have effectively unlimited storage and compute capacity. Eventually all disclosed data will be at least statistically correlatable.
Weak Privacy
un-correlated interactions over bounded time and space.
When the cost of correlation exceeds the value of correlation the data will become un-correlated (de-correlated).
Economics of Correlator predictive value 1 B s 8 H 5 8 8 time data lifecycle past now future measured predicted
Economics of Correlator: Value Extraction predictive value ] B ] 8 H E 2 s e g 5 extractafv/e value g time data lifecycle past now future measured predicted
Economics of De-correlator g predictive value E 7 5 8 b= c g £ 8 ] $ ] S time data lifecycle past now future measured predicted
Economics of De-correlator: Value Extraction s predictive value 3 K 8 5 extractable value 5 3 AN s £ 5 g g E 3 time data lifecycle past now future measured predicted
Operating Regimes o Regulation H Hide or Bribe and EH Contracts & H 2 £ Hide and Bribe Reputation E) g Tilegitimate Legitimate Economic
Freedom balanced
Freedom from ... Freedomto ... exploitation (commercial) extract value(commercial) intimidation (political) build relationships (social) censorship (political) build community (political)
possibility of erasure = possibility of censorship
anonymity = loss-of-value from attribution fairness = requires data attribution