Consent Assemblies Overview

• Consent assemblies are composite structures that integrate individual yes/no consents with collective, deliberative policy rules to regulate data processing across digital ecosystems.
• They employ formal models, interactive protocols, and blockchain-based audit trails to ensure compliance with GDPR and scalable data stewardship.
• Empirical findings reveal that design elements like consensus thresholds and interface protocols significantly influence consent rates and policy enforcement.

Consent assemblies are composite consent structures—ranging from a collection of individual, contextualized yes/no grants to collective, deliberatively authored policy rules—used to regulate data processing, disclosure, and access across digital ecosystems. They are implemented via formal models, interactive protocols, policy algebra, and, in emerging settings, deliberative mini-publics empowered to set default governance rules. Consent assemblies underpin critical infrastructure for GDPR-aligned data stewardship, privacy management, and scalable trust in public digital infrastructures.

1. Formal Definitions and Core Models

Consent assemblies can be formalized at several scales, from atomic, algebraic pairing (in IoT and e-health architectures) to threshold-based collective decision processes.

• Individualized Assembly (IoT, Web, Healthcare): A consent assembly is the dynamic pairing of a data controller's declared policy $\pi_c$ and a data subject's selected policy $\pi_s$, evaluated for compliance at data collection. Formal models use state-transition functions capturing device locations, policy overrides, and algebraic implication ordering ($\pi_1 \succ \pi_2$). Consent is only valid if all preconditions (policy compatibility, user knowledge, event constraints) are satisfied. Deployment architectures extend to registries, policy repositories, and composable protocol stacks (Cunche et al., 2018, Pardo et al., 2024, Asghar et al., 2013).
• Collective Consent Assembly: Defined as a deliberative mini-public $\mathcal{A} = \{a_1, \dots, a_n\}$ representing broader populations. Each member votes $v_i \in \{0,1\}$ on a proposed data processing purpose $p$, and the group decision follows consensus thresholds:

$$C(p) = \begin{cases} 1 & \text{if } \big( \frac{1}{n} \sum_{i=1}^{n} v_i \big) \geq \tau_{\text{accept}} \ 0 & \text{if } \big( \frac{1}{n} \sum_{i=1}^{n} v_i \big) \leq \tau_{\text{reject}} \ ? & \text{otherwise} \end{cases}$$

For example, $\tau_{\text{accept}} = 0.75$, $\tau_{\text{reject}} = 0.25$, encoding both binding and conditional/negotiated consent outcomes (Kyi et al., 23 Jan 2026).

• Resource Ownership and Flow Algebra: In DPI, consent assemblies are sequences of ownership and flow primitives acting on X-nodes: i-nodes (information), s-nodes (shadow/conferred rights), and v-nodes (virtual, time-limited rights). Four key operators—SHARE, CONFER, TRANSFER, COLLATERAL—compose elementary transitions. Provenance and post-conditions guarantee enforceability and auditability (Vaidyanathan et al., 4 Nov 2025).

2. Mechanisms and Protocols for Assembly

Consent assembly workflows rely on well-specified transition logics, contextual triggers, and compositional policy algebra.

• IoT Frameworks: Consent is operationalized through seven core operations—install, declare, move, define, collect, pair, require—forming Hoare-logic style state transitions. Devices engage via BLE advertisements or REST/HTTPS protocols, with policy evaluation performed inline on DS and DC gateways, guaranteeing that only compliant policy pairs result in data flow (Cunche et al., 2018).
• Teleo-Reactive Consent Policies (Healthcare): Consent assemblies in ACTORS use TR programming: a prioritized list of rules, each with a context-sensitive Boolean guard $C_i(\Sigma)$ and corresponding action $A_i$. Policy templates parameterized on context are instantiated, activated, or withdrawn in reaction to situational changes. The unique transition $$(\text{instantiatePolicy}(Tpl), \Sigma) \Longrightarrow P$$ only holds if all contextual predicates are satisfied (Asghar et al., 2013).
• Blockchain Consent Assembly: Using Hyperledger Fabric, consent assemblies are represented as a conjunction of Boolean flags in consent assets. Smart contracts record, update, and query consent states, with policies composed via asset registry queries and updates (atomic granular revocation is always available). Auditing is achieved via immutable transaction history and endorsement protocols (Aldred et al., 2019).
• Collective Deliberation: Consent assemblies here are structured through stratified sortition, expert learning sessions, facilitated negotiation, and formalized voting rules. Conditional consent clauses and dissenting rationales are included in the published record (Kyi et al., 23 Jan 2026).

3. Quantitative Findings and Policy Compliance

Empirical evidence demonstrates both benefits and pitfalls in real-world assembly implementations.

• CMP Scraping and Experimentation: Among 680 UK websites deploying the top 5 CMPs, only $11.8\%$ met all three GDPR-mandated criteria: explicit opt-in, equal accept/reject accessibility, no pre-ticked boxes. Notably, removing the “Reject All” button on the first page elevated the “Accept All” rate by $22$–$23$ percentage points, while fine-grained controls on the first page dropped consent by $8$–$20$ points. Notification style (banner vs. barrier) showed no significant consent effect ($F(1,279)=0.000$, $p=1.000$) (Nouwens et al., 2020).
• GDPR Model Checking: TLA+ model-checking confirms that technical enforcement of DS policy compliance and informed consent holds—no data item ever possesses more permissive processing rights than prescribed by the data subject. Both invariant checks and refinement mappings across direct (web CMP) and indirect (IoT BLE) architectures are discharged with no violations (Pardo et al., 2024).
• Blockchain System Metrics: Prototype systems demonstrate O(1) per-request complexity for operator actions; POST/PUT consent changes incur $2$–$4$ second latency per transaction, scale-out is feasible via peer replication and REST-layer horizontal sharding (Aldred et al., 2019, Vaidyanathan et al., 4 Nov 2025).

4. Practical Guidelines, Implementations, and Case Studies

Best practices for consent assembly formation and enforcement are domain-specific:

• CMP/UI Design: Both “Accept All” and “Reject All” buttons should always appear on the initial interface layer, with equal click and visual complexity. Granular purpose/vendor selections may be deferred to secondary layers, while defaults for optional processing must be off. Implied consent via scrolling, navigation, or pop-up dismissal is prohibited (Nouwens et al., 2020).
• IoT Case Studies: BLE Privacy Beacons, Wi-Fi trackers, and ANPR deployments use automatic pairing and algebraic policy checks to guarantee only assembly-compliant data collection. Smart meeting-room sensors activate only when each occupant’s consent is assembled and verified (Cunche et al., 2018).
• E-Health ACTORS Implementation: Policy instantiation, withdrawal, and activation are tied to context streams (clinic visits, referrals, emergencies), ensuring that only goal-compliant, contextually valid consent policies govern data flows (Asghar et al., 2013).
• Blockchain Auditability: Consent revocation, right to erasure, and rectification are implemented via atomic chaincode operations and linked off-chain storage. Traceability and non-repudiation are guaranteed by possession of hashed, signed consent assets (Aldred et al., 2019).
• Collective Assembly Vignettes: Replacing cookie banner systems, assemblies composed of stratified delegates issued binding defaults for "UX improvement" data ($82\%$ support) and prohibited covert A/B experimentation ($78\%$ opposition). GenAI model training assemblies led to collective rejection (only $18\%$ support), prescribing no retrospective AI data use absent new explicit consent (Kyi et al., 23 Jan 2026).
• DPI Consent Flow Example:

| Step | Operator | Result | |--------|------------|--------------------------| | 1 | CONFER | Transcript issued, locked| | 2 | SHARE | V-node issued, 30-day | | 3 | SHARE | Verification v-node | | 4 | COLLATERAL | Pledge and shadow created| | 5 | REVERT | Pledge/shadow revoked | Traceability is enforced via provenance logs (Vaidyanathan et al., 4 Nov 2025).

5. Regulatory, Scalability, and Future Directions

Consent assemblies encounter complex challenges in regulation, scalability, and social acceptance:

• Regulatory Alignment: Current regulations (GDPR, CCPA) presume individualized notice and consent. Scaling to collective assemblies requires explicit legal amendments—potentially via new codes of conduct and enforcement mechanisms (Article 40/41). Joint-controller liability for upstream CMP vendors and automated compliance scanners are advocated for scalable enforcement (Kyi et al., 23 Jan 2026, Nouwens et al., 2020).
• Scalability and Performance: Operator algebra in DPI architectures supports millions of consent flows per minute; audit tokens and consent provenance grow linearly with transaction count but remain manageable. Comprehensive automation for cross-jurisdictional rule composition, high-volume enforcement (watermarking, browser policies), and adversarial defenses are active research topics (Vaidyanathan et al., 4 Nov 2025, Aldred et al., 2019).
• Collective Governance and Trust: Shifting from individual to assembly-based governance faces ideological hurdles. User trust depends on representativeness, transparency, documented dissent, and opt-out channels. Future systems may employ secure remote deliberation (homomorphic tallying, federated identity-proofing) (Kyi et al., 23 Jan 2026).
• Correctness and Adaptability: Dynamically evaluated TR policies (ACTORS) and formal model-checking (TLA+) ensure both correctness (no conflict or ambiguity in authorization) and adaptability (transparent policy evolution as requirements, context, or regulation change) (Pardo et al., 2024, Asghar et al., 2013).

6. Significance and Emerging Controversies

Consent assemblies represent a foundational shift in data governance—from atomized, often dark-pattern-laden opt-in interfaces to compositional, auditable, and increasingly collective paradigms. The tension between granular user autonomy and scalable data stewardship is central; a plausible implication is that future regulatory and technical standards may favor assembly-based consent structures in domains where individual consent is infeasible or insufficiently robust. Key open controversies include practical implementation of representativity in assemblies, cross-border regulation for DPIs, and balancing usability against regulatory specificity.

By operationalizing consent assemblies using compositional algebra, deliberative governance, blockchain audit trails, or context-adaptive policy engines, digital systems can robustly enforce privacy rights while supporting scalable, legally compliant data flows (Kyi et al., 23 Jan 2026, Vaidyanathan et al., 4 Nov 2025, Cunche et al., 2018, Nouwens et al., 2020, Asghar et al., 2013, Aldred et al., 2019, Pardo et al., 2024).