Protocol Architecture

1.1 The Need for a New Identity Architecture

The modern internet is built on:

accounts

credentials

passwords

physical identifiers

platformowned identity

These primitives fail in the simulation age because:

AI can generate infinite identities

physical identifiers can be deepfaked

accounts can be masscreated

behavior can be simulated

institutions cannot distinguish humans from AI

Identity must evolve from:

platformowned accounts → sovereign geometric structures.

1.2 The MyShape Architectural Thesis

The MyShape Protocol is built on three core principles:

Derived from the irreducible structure of biological motion.

Generated locally, never stored, never extractable.

Proven through zeroknowledge, verifiable anywhere.

These principles define the architecture.

1.3 Architectural Goals

The protocol must:

distinguish humans from AI

verify presence in real time

prevent Sybil attacks

preserve privacy

operate across all platforms

run on consumer hardware

support both humans and AI agents

scale to billions of identities

This requires a layered, modular, cryptographically grounded architecture.

2.1 Locality

All sensitive computation occurs ondevice:

motion capture

invariant extraction

manifold projection

integrity scoring

ZK proof generation

Nothing sensitive leaves the device.

2.2 NonExtractability

The protocol never stores or transmits:

raw motion

physical identifiers

individual geometry

manifold coordinates

kinematic tensors

Identity is proven, not revealed.

2.3 Succinctness

All proofs must be:

<512 bytes

verifiable in <1ms

compatible with blockchain, mobile, XR, IoT

Identity must be portable.

2.4 Universality

The protocol must run on:

phones

laptops

XR headsets

robots

browsers

blockchains

Identity must be universal.

2.5 AntiSynthesis

The architecture must resist:

AIgenerated motion

replay attacks

sensor spoofing

scripted motion

avatar puppeting

Identity must be real.

2.6 Agent Coexistence

The architecture must support:

human identities

AI identities

hybrid identities

multiagent systems

Identity must be multispecies.

The MyShape Protocol is composed of five verticallystacked layers, each responsible for a distinct phase of identity generation, verification, and proof construction.

Code

+-----------------------------------------------------------+

| LAYER 5 — IDENTITY LAYER |

| Sovereign Identity • Presence • Liveness • Sybil Defense |

+-----------------------------------------------------------+

| LAYER 4 — PROOF LAYER |

| ZK-MG • ZK-MIP • Succinct Verification • Cross-Platform |

+-----------------------------------------------------------+

| LAYER 3 — INTEGRITY LAYER |

| Anti-Synthesis • Anti-Replay • Sensor Fusion • Noise |

+-----------------------------------------------------------+

| LAYER 2 — GEOMETRY LAYER |

| Invariants • Manifold Projection • Canonical Structure |

+-----------------------------------------------------------+

| LAYER 1 — CAPTURE LAYER |

| Real-Time Motion Capture • Sensor Input • Preprocessing |

+-----------------------------------------------------------+

Each layer is:

modular (replaceable without breaking the system)

local (runs on-device)

privacy-preserving (no sensitive data leaves the device)

cryptographically grounded (outputs verifiable truth)

Together, they form a sovereign identity substrate for the simulation age.

The Capture Layer is responsible for acquiring raw motion signals from consumer hardware.

It is designed to be:

hardwareagnostic

lowlatency

robust to noise

privacypreserving

4.1 Supported Sensors

The Capture Layer supports:

RGB cameras

depth cameras

IMUs (accelerometer + gyroscope)

LiDAR (optional)

skeletal tracking APIs (ARKit, MediaPipe, OpenXR)

The system is designed to degrade gracefully:

if depth is missing → fallback to RGB

if IMU is missing → fallback to optical flow

if skeletal tracking is lowquality → fallback to multiview estimation

4.2 Raw Signal Types

The Capture Layer collects:

joint positions

joint velocities

joint accelerations

optical flow fields

IMU readings

depth maps

timestamp streams

These signals form the basis of all downstream geometry and integrity checks.

4.3 Preprocessing Pipeline

Preprocessing includes:

denoising

temporal smoothing

coordinate normalization

root alignment

scale normalization

orientation normalization

This ensures consistent geometry across devices and environments.

4.4 Privacy Guarantees

The Capture Layer:

stores nothing

uploads nothing

transmits nothing

retains nothing

All raw signals are ephemeral.

The Geometry Layer transforms raw motion into identity geometry.

This is where identity becomes mathematical.

5.1 Kinematic Feature Extraction

From normalized motion M~, we compute:

relational joint vectors

angular velocities

curvature signatures

phase relationships

microkinematic jitter

These form the kinematic tensor K.

5.2 Geometric Invariant Encoding

We compute invariants:

G=ϕ(K)

Invariants remove:

scale

rotation

translation

temporal phase

This ensures identity is structural, not visual.

5.3 Canonical Manifold Projection

We project invariants into a canonical manifold:

M=Π(G)

The manifold:

is unique to the user

is nonextractable

is nonreconstructable

is nontransferable

This is the geometric identity.

5.4 Manifold Stability

The manifold must be:

stable across time

stable across devices

stable across environments

We enforce:

d(Mt,Mt+1)<ϵ

This ensures identity continuity.

5.5 Privacy Guarantees

The Geometry Layer:

never stores the manifold

never transmits the manifold

never exposes invariants

never exposes kinematic tensors

Only proofs leave the device.

The Integrity Layer ensures that motion is:

real

live

embodied

nonsynthetic

nonreplayable

sensorconsistent

This is the security core of the protocol.

6.1 Temporal Integrity

We validate:

derivative continuity

acceleration continuity

jerk continuity

timestamp monotonicity

framerate stability

Replay attacks fail here.

6.2 Kinematic Integrity

We validate:

biomechanical constraints

joint limits

curvature signatures

phaselocked relationships

Scripted motion fails here.

6.3 Noise Integrity

We validate:

biological jitter

stochastic variance

IMU–vision noise correlation

AI motion fails here.

6.4 Sensor Fusion Integrity

We validate:

RGB–depth consistency

optical flow–skeleton consistency

IMU–vision correlation

Sensor spoofing fails here.

6.5 AntiSynthesis Integrity

We detect:

diffusion motion

GAN motion

transformer motion

physicssimulated motion

Synthetic motion fails here.

6.6 Integrity Score

We compute:

Sintegrity=f(Stemporal,Skinematic,Snoise,Sfusion,SantiSynth)

Only highintegrity motion is allowed into the Proof Layer.

The Proof Layer transforms embodied geometry + integrity signals into succinct, verifiable, zeroknowledge proofs.

This is the layer where identity becomes cryptographically real.

7.1 Proof Types

The Proof Layer supports two primary proof systems:

Proves:

the user generated valid motion

the motion matches their manifold

the motion is nonsynthetic

the motion is nonreplayable

without revealing:

motion

geometry

invariants

manifold coordinates

Proves:

integrity score ≥ threshold

sensor fusion passed

antisynthesis checks passed

temporal continuity passed

without revealing:

raw signals

noise patterns

device signatures

7.2 Proof Generation Pipeline

The pipeline:

Input:

kinematic tensor

invariants

manifold projection

integrity scores

Witness Construction:

compressed, nonreconstructable

no raw motion

no physical identifiers

Circuit Execution:

PLONKish arithmetization

custom gates for kinematics

lookup tables for invariants

Proof Output:

128–512 bytes

verifiable in <1ms

platformagnostic

7.3 Verification

Verification is:

constant time

deterministic

crossplatform

blockchaincompatible

browsercompatible

XRcompatible

This enables universal identity verification.

7.4 Privacy Guarantees

The Proof Layer:

reveals nothing

leaks nothing

stores nothing

exposes nothing

Identity is proven, not shown.

The Identity Layer is the top of the protocol stack. It defines what identity is in the MyShape ecosystem.

Identity is:

sovereign

embodied

geometric

cryptographic

nonsynthetic

nonextractive

8.1 Identity Object

An identity is represented as:

Code

Identity = {

public_key,

presence_proof,

geometry_proof,

integrity_proof,

optional_metadata

}

It does not include:

motion

physical identifiers

manifold

invariants

personal data

8.2 Presence

Presence is proven by:

realtime motion

integrity score

ZKMIP

Presence is:

continuous

embodied

cryptographically verifiable

8.3 Liveness

Liveness is proven by:

biological noise

temporal continuity

antireplay constraints

Liveness is:

nonsynthetic

nonreplayable

nontransferable

8.4 Sybil Resistance

Sybil resistance emerges from:

nonreplicable geometry

nontransferable manifolds

nonsynthetic motion

One body → one identity.

8.5 CrossPlatform Identity

Identity works across:

social networks

virtual worlds

governance systems

financial platforms

XR environments

robotics

IoT

Identity becomes portable.

The MyShape Protocol is designed to operate across heterogeneous environments.

9.1 OnDevice Runtime

Runs:

Capture Layer

Geometry Layer

Integrity Layer

Proof Layer

Outputs:

ZK proofs

presence signals

liveness signals

9.2 CloudOptional Verification

Verification can run:

onchain

in browsers

in games

in XR

in mobile apps

in robots

No cloud dependency.

9.3 Interoperability

The protocol exposes:

WASM modules

Rust libraries

TypeScript bindings

Unity/Unreal SDKs

iOS/Android native APIs

Identity becomes composable.

9.4 Agent Compatibility

Supports:

human identities

AI identities

hybrid identities

AI agents can:

declare themselves

sign actions

verify human presence

This enables multispecies ecosystems.

The security model defines what the protocol protects against.

10.1 Threats Mitigated

AIgenerated motion

replay attacks

sensor spoofing

scripted motion

avatar puppeting

identity forgery

Sybil attacks

10.2 Threats Not Mitigated

device compromise

OSlevel malware

camera obstruction

physical coercion

These must be handled by the device ecosystem.

10.3 Formal Guarantees

The protocol guarantees:

ZeroKnowledge Privacy

NonReplicability

NonTransferability

AntiSynthesis

AntiReplay

Succinct Verification

CrossPlatform Validity

The MyShape Protocol defines a new identity primitive for the simulation age:

Identity is geometry. Geometry is presence. Presence is truth.

The architecture integrates:

realtime motion capture

geometric invariants

manifold projection

integrity verification

zeroknowledge proofs

sovereign identity

This creates a civilizational identity substrate that is:

uncopyable

nonsynthetic

privacypreserving

platformagnostic

cryptographically verifiable

MyShape is not an application. It is a new identity layer for the future of humanity and AI.

APPENDIX

A. Data Flow Diagram (TextBased)

Code

CAPTURE → GEOMETRY → INTEGRITY → PROOF → IDENTITY → VERIFICATION

B. Interface Definitions

Capture API

Geometry API

Integrity API

Proof API

Identity API

C. Security Tables

Attack types → mitigations → failure modes.

D. Implementation Notes

ondevice optimization

GPU acceleration

WASM constraints

XR integration

PAPER_07 — ECON_ARCHITECTURE

THE MYSHAPE TOKENOMICS ARCHITECTURE: VALUE FLOWS FOR A GEOMETRIC IDENTITY CIVILIZATION

REV_V1.0 — FULL WHITEPAPER

ABSTRACT

This paper presents the full tokenomics architecture of the MyShape Protocol, a sovereign identity system built on geometric presence, realtime motion integrity, and zeroknowledge proofs. Unlike traditional token models that incentivize speculation or platform usage, the MyShape economic system is designed to:

reward embodied presence

price computational verification

capture protocollevel value

enable crossplatform identity portability

support human–AI multiagent ecosystems

The architecture is built on a threetoken system:

$PULSE — the Body Token Rewards embodied presence and realtime motion proofs.

$ENERGY — the Computation Token Fuels verification, ZK proof generation, and protocol operations.

$SHAPE — the Governance & Value Capture Token Captures longterm protocol value and governs system evolution.

Together, these tokens form a closed economic loop:

Body → Computation → Protocol → Body

This paper defines the issuance, flows, sinks, incentives, governance, and security properties of the MyShape token economy.