ZipperChain Infrastructure

• ZipperChain Infrastructure is a distributed ledger technology that uses a specialized pipeline of trusted services to ensure immutability, agreement, and availability.
• It employs a linear chain of components—Queue, ZipIt, Timestamp, Sequencer, and Replication services—to achieve sub-second block finality and high throughput (≈18,183 TPS).
• The framework eliminates native token requirements and complex consensus protocols, relying on robust cloud infrastructure and simplified auditability for security.

ZipperChain Infrastructure is a distributed ledger technology (DLT) framework that guarantees immutability, agreement, and availability of transaction data through a linear pipeline of specialized, trusted third-party services rather than through distributed consensus mechanisms. Its infrastructure organizes transaction processing in a centralized manner using high-assurance services, supporting sub-second block finality and transaction throughput at network line-rates, and obviates the requirement for a native token or incentive system (Bjornsson et al., 26 Nov 2025).

1. Pipeline Architecture and Component Roles

ZipperChain replaces consensus-based protocols with a chain of components coordinated by the ZipIt orchestrator, deployed over fast data center links. Transactions are processed through a series of services:

• Queue Service: Implements FIFO queuing of submitted transaction data (e.g., Redis). Primary API: enqueue(d), dequeue(batchSize).
• ZipIt Service: Stateless, single-threaded controller per pipeline. Dequeues transactions, composes Merkle trees, and packages blocks. Key functions: makeMerkleTree(transactions); makeBlock(m, prevTimestampLink).
• Trusted Timestamp Service (T): Issues real-world, cryptographically signed timestamp attestations using OAuth/OIDC providers. API: timestamp(L(x)), validate(K₊, t).
• Trusted Sequencer Service (S): Guarantees strictly ordered sequence numbers via AWS Nitro Enclave. API: sequence(L(x)), check(Kₛ, s).
• Trusted Replication Service (R): Durably replicates Merkle trees, blocks, and attestations using WORM cloud storage (6 erasure-coded shards; recoverable from any 3 shards) across diverse providers.

The pipeline construction enables ZipIt to fan out calls in parallel where feasible; block finalization is signaled upon durable replication of all artifacts. Data flow for a block $b_i$ progresses through Merkle tree formation, block packaging, notarization, sequencing, and cross-region replication.

2. Trust Model and Security Assumptions

ZipperChain's model concentrates trust in the correct operation of a small number of well-audited, third-party services rather than in open network participants. The boundaries of trust are:

• Timestamp Service (T): Must safeguard its key and maintain a synchronized clock.
• Sequencer Service (S): Must protect private key, persist counter state (no resets), and operate untampered within an enclave.
• Replication Service (R): Enforces WORM and shard distribution for durability and immutability.

The fault model tolerates single-point compromise; for example, downtime or compromise of any single service halts liveness but preserves chain integrity and immutability. Only a simultaneous break of T's key, S's enclave, and majority of replication shards poses systemic risk—a scenario considered highly improbable. Users must correctly obtain and pin public service keys and configuration attestations.

Service	Trust Requirements	Fault Impact
Timestamp	Private key, real-time clock	Liveness stalls
Sequencer	Private key, enclave integrity	Liveness stalls
Replicator	WORM, shard distribution	Availability loss

3. Atomic Broadcast and Chain Construction

At its core, ZipperChain produces a totally ordered, immutable chain of batch transactions (blocks), each associated with distinct attestations:

• Transaction Link: $d = (\text{schema}, \text{type}, L(x))$
• Merkle Tree: $m = (u, v = [d, ...])$
• Block: $b = (u, mL, tL)$
• Timestamp Attestation: $$t = (y = L(b), p, u, g = \text{Sign}_k(H(y, p, u)))$$
• Sequence Attestation: $s = (y = L(t), c, g = \text{Sign}_k(H(y, c)))$

Each finalized block is represented as triad $A = (b, t, s)$. The protocol enforces four invariants (trueTriad):

1. $A.t.y == L(A.b)$
2. $\text{validate}(K_+, A.t)$ is true
3. $A.s.y == L(A.t)$
4. $\text{check}(K_s, A.s)$ is true

Ordering is determined by triad height and sequence counter, enabling rigorous fork resolution and main-chain selection. Certificate generation for transaction proofs traverses the main chain by Merkle contents.

4. Performance Analysis

ZipperChain achieves near line-rate throughput and low block finality latencies due to service co-location and pipeline architecture:

• Block Finality: Mean ≈ 427 ms (no load), ≈ 805 ms with 250 client load ($p_{90} < 1$ s)
• Throughput: ≈ 18,183 transactions/s (250 clients)

Latency components are precisely measured:

$$L_{total} \approx L_{enqueue} + L_{queue\_deq} + L_{merkle\_build} + L_{block\_pack} + L_{rep}(m) + L_{timestamp} + L_{rep}(b, t) + L_{sequence} + L_{rep}(s) + L_{ack\_network}$$

Erasure coding and multi-region uploads dominate latency under load. Pipeline parallelism mitigates bottlenecks except for IO bound stages, aligning finality with network and cloud service characteristics.

5. Deployment and Scaling Considerations

Hardware recommendations observed in implementation:

• ZipIt: t2.xlarge (4 vCPU, 16 GiB)
• Sequencer Enclave: c5a.xlarge, plus proxy
• Queue: Redis, similar instance
• Replication: WORM storage across six global regions/providers

All pipeline services should be co-located for low-latency ($\sim$1 ms RTT), with replication providing independence and durability (35-nines over 100 years). Durability/latency trade-offs are managed via erasure shard counts.

ZipIt scales horizontally via namespace partitioning; each instance maintains its own sequencer enclave and key/counter state. Sequencer crash or software update necessitates enclave replacement protocols to avoid forks and counter discontinuity, as described in a companion paper.

6. Comparison with Consensus-Based DLTs

ZipperChain departs fundamentally from consensus-based blockchains:

• Consensus: Linear pipeline; no voting, mining, or token incentives.
• Performance: Sub-second finality, ≈ tens of thousands TPS; typical blockchains: $\geq$ 1 s/block, $<$ 1,000 TPS.
• Resource Requirements: Centralized data center footprint, standard VMs/TEEs, cloud storage.
• Complexity: Code paths are notably simpler; auditability improved; consensus protocols and P2P overlays are eliminated.
• Trust Model: Relies on assurances from cloud infrastructure; 51% attack vectors do not apply.
• Tokenization: No native token; transaction costs paid off-chain.

A plausible implication is that ZipperChain is especially suited to environments prioritizing verifiable efficiency and centralized operational control over permissionless participation or token-based incentives.

7. Conclusion and Research Significance

ZipperChain demonstrates that atomic broadcast guarantees—immutability, agreement, and availability—can be achieved by leveraging constrained trust in high-assurance, third-party services and structuring block creation as a controllable pipeline rather than via traditional distributed consensus. The architecture offers significantly improved performance and simplified deployment for distributed ledgers, at the cost of reconfiguring trust assumptions to external service providers rather than network participants. Sub-second finality, line-rate throughput, and the absence of native token economics distinguish ZipperChain within the DLT landscape (Bjornsson et al., 26 Nov 2025).