Soft Information Sharing Protocol

• Soft Information Sharing Protocol is a communication framework that exchanges uncertainty-quantified data such as log-likelihood ratios to support cooperative decision-making.
• It integrates mechanisms like access control, encrypted channels, and event-triggered updates to securely share soft data in diverse applications including wireless networks and quantum computing.
• The protocol enhances system performance by enabling reliable data aggregation, dynamic incentive alignment, and reduced error rates, thereby improving overall network resilience.

A Soft Information Sharing Protocol is any communications and control protocol wherein agents, systems, or applications exchange information featuring explicit or implicit uncertainty quantification (soft information)—typically in the form of log-likelihood ratios (LLRs), probabilistic confidences, or metadata indicating partial state, reliability, or context—in order to enable cooperative decision-making, distributed inference, or resilient functionality. Protocols of this type arise in secure key exchange, multi-agent learning, quantum information processing, wireless error correction, and privacy-preserving data exchange, with technical features encompassing access control, dynamic incentive mechanisms, event-triggered data exchange, and soft decoding/aggregation.

1. Theoretical Foundations and Definitions

Soft information, as formalized in coding theory, quantum information, and distributed systems, is non-binary, quantitative data attached to outcomes or messages that indicate degrees of confidence, reliability, or risk. In protocol design, "soft" contrasts with hard-decision (fully resolved, deterministic) information—enabling participants to share evidence or estimates reflecting underlying uncertainty.

Mathematically, soft information is often expressed as LLRs: $\tilde{x}_m = \log\frac{P(x_m=0)}{P(x_m=1)}$ for a bit $x_m$ in communication systems, or as explicit probabilities, scores, or attributed weights in multi-agent inference.

Within soft information sharing protocols, participants use this uncertainty-quantified information both to adapt their local decision-making and to drive cooperative mechanisms such as incentive alignments, reputation management, or confidence-weighted aggregation.

2. Protocol Classes and Architectures

Soft information sharing protocols may be categorized according to the architectural domain and functional goal:

1. Networked Distributed Systems: PeerShare (Nagy et al., 2013) enables applications to share sensitive (soft) information—public/private keys, secret bindings, device IDs—selectively with social contacts. Access control exploits social graph interfaces, SSO/OAuth authentication, and restricts visibility to authorized recipients, with client-server role separation. All data exchanges are secured by TLS/HTTPS with certificate pinning, and per-data-item access policies may be tied to friend lists or explicit recipient lists. The model is formally defined as releasing $d$ to $u'$ iff $S(u') \in \text{Recipient}(d)$, where $S(u')$ is the social identifier.
2. Privacy-Preserving Information Exchange: TIPS (Pasumarthy et al., 8 Mar 2024) orchestrates fine-grained, privacy-aware cyber threat information (soft CTI) sharing between organizations by integrating Attribute-Based Encryption (ABE), Homomorphic Encryption (HE), and Zero Knowledge Proofs (ZKP) on Hyperledger Fabric. Trusted channels, key management, per-message access policies, and trusted deletion mechanisms handle compliance and auditability.
3. Wireless Networking and Error Correction: SSIC (Soft-Source Information Combining) (Zhang et al., 2022) leverages LLRs extracted from multiple packet streams (even when undecodeable via CRC) and aggregates soft outputs after descrambling to enable ultra-high reliability by combining partial evidence.
4. Incentivized Multi-Agent Learning: In distributed multi-agent systems (Xu et al., 2013), rating protocols use soft information about agent compliance to adjust future information access—high ratings unlock more information sharing, low ratings act as punishment—implementing indirect reciprocity adapted to topology, heterogeneity, and time-varying participants.
5. Cooperative Task-Based Soft Sharing: The post-task-completion protocol (Dutta et al., 2011) involves agents asynchronously exchanging state information only after collaborative task completion, leading to "soft," event-driven updates that minimize communication overhead and maximize utility for tasks such as cooperative routing.
6. Quantum Information Processing: In quantum surface code environments (Akahoshi et al., 24 Oct 2025), runtime reduction protocols utilize decoder-generated soft information (time-like LLR gaps for logical measurement error) to dynamically determine measurement adequacy, adaptively requesting further measurements only if confidence falls below threshold.

3. Key Mechanisms and Algorithms

The effectiveness of soft information sharing protocols depends on several critical mechanisms:

• Credentialing and Access Control: OAuth 2.0, ABE, and role-based policies secure which parties may access or modify shared information. PeerShare records per-app ownership metadata and checks at both client and server.
• Information Encoding and Transport: Soft information is serialized in protocol-specific representations (JSON, LLR arrays, log-likelihood tables), and transported over encrypted channels (TLS/HTTPS, permissioned blockchain channels).
• Combining Algorithms: In the SSIC protocol, individual soft bit estimates across replicas are aligned (via Soft Descrambling SD algorithms) and LLRs summed: $$\tilde{x}_m^\text{(agg)} = \sum_{n=0}^{N-1} \tilde{x}_m^{(n)}$$ Decision rules based on the aggregated soft value then drive delivery or rejection.
• Incentive and Reputation Structures: In rating protocols for strategic agents (Xu et al., 2013), local Social Network Interfaces (SNIs) recommend how much to share with each neighbor based on their ratings; ratings are updated probabilistically according to compliance (e.g., via transition rules $\tau_i$).
• Event-Driven vs. Synchronous Sharing: Protocols like post-task-completion (Dutta et al., 2011) rely on event triggers (task completion) rather than continuous or periodic updates, which reduces delay and bandwidth use, especially in high-diameter topologies.
• Privacy and Compliance Techniques: ABE and ZKP in TIPS ensure access is attribute-bound and privacy is maintained, while HE permits privacy-preserving aggregation across organizational domains.

4. Performance, Security, and Efficiency

The rigorous evaluation of soft information sharing protocols highlights several properties:

• Security Properties: Confidentiality (channel encryption, end-to-end key management), integrity (TLS, enforced access control), and authenticity (identity checks via OAuth, PKI, or blockchain MSP).
• Access Control Granularity: PeerShare and TIPS both allow per-item or per-message policies, resolved conditionally at runtime.
• Timeliness and Estimate Quality: In cooperative agent settings (Dutta et al., 2011), post-task completion sharing ensures fresher knowledge for global state, outperforming nearest-neighbor or stepwise protocols; message overhead is reduced to as little as $0.25\times$ competitive baselines.
• Reliability Gains: In wireless SSIC (Zhang et al., 2022), integrating multi-path soft LLRs yields $>4\times$ lower packet loss under interference, with up to 99.99% successful packet delivery in practice without application-level changes.
• Runtime Reduction in Quantum Settings: Adaptive protocol in lattice surgery (Akahoshi et al., 24 Oct 2025) using time-like soft information achieves runtime reductions of over 50% compared to naive serial approaches, especially in hybrid protocols (STELS).
• Scalability and Locality: Distributed dual decomposition and local SNI computation (Xu et al., 2013) ensure equilibrium can be maintained in large, dynamic agent networks—even with asymmetric topologies and partial observability.

5. Mathematical Formulation and Protocol Summaries

Representative mathematical formalizations are central in protocol definition:

• Eligibility for Access (PeerShare):

$\text{Recipient}(d) = \{ S(u')~|~u' \in P(d) \}$

• Soft Combining Rule (SSIC):

$$\tilde{x}_m^\text{(agg)} = \sum_{n=0}^{N-1} \tilde{x}_m^{(n)}$$

Final hard decision: $\hat{x}_m^\text{(final)} = 0$ if $\tilde{x}_m^\text{(agg)} \geq 0$, $1$ otherwise.

• Post-Task-Completion Update (PTC):

$$Q_i(n, i+1) \leftarrow (1-\alpha)Q_i(n, i+1) + \alpha R_i$$

Where $R_i$ is the minimum (PTC-M) or average (PTC-A) of downstream state.

• Quantum Soft Metric (Lattice Surgery):

$$\Delta_{\mathcal{T}}(s) \equiv \bar w_{l_\text{wrong}(s)} - \bar w_{l_\text{correct}(s)}$$

Conditional error probability: $P(\mathcal{T} \mid g) = \frac{1}{1 + e^{ag}}$

6. Applications and Real-World Usage

• Key and Credential Sharing: PeerShare supports multi-application sharing of secrets and credentials (public keys, device IDs) among user-specified friends, with tightly bound platform application identification.
• Cyber Threat Intelligence Exchange: TIPS offers compliant, privacy-preserving sharing of enriched CTI, supporting right-to-be-forgotten, redactment, and federated analytics over encrypted datasets.
• Ultra-Reliable IoT and Industrial Wireless: SSIC enables deployment of commodity hardware for mission- or safety-critical wireless links where traditional duplicate transmission approaches are insufficient under correlated noise.
• Distributed Multi-Agent Learning and Control: Post-task-completion and rating-based protocols enable robust, scalable cooperative behavior—e.g., in routing, estimation, and distributed consensus—especially where participants are autonomous, self-interested, or subject to dynamic topology.
• Quantum Algorithms and Lattice Surgery: Time-like soft information-driven protocols provide operational runtime reduction on quantum hardware, applicable across circuit types and logical operation scheduling strategies.

7. Technical Limitations, Challenges, and Outlook

• Trust Model Constraints: PeerShare and similar frameworks require a trusted server, though this trust can be mitigated by deploying hardware security modules or decentralized variants.
• Scalability of Attribute and Policy Management: Fine-grained ABE and role-based access policies can become complex in large-scale sharing platforms, impacting both user experience and administrative overhead.
• Robustness to Monitoring/Compliance Errors: Incentive and rating protocols must accommodate imperfect monitoring (false positive/negative compliance signals), necessitating optimal parameterization of update probabilities.
• Soft Information Quality and Synchronization: The efficacy of aggregation relies on accurate characterizations of uncertainty and the ability to synchronize state or interpret overlapping soft evidence streams.
• Quantum Readiness: Runtime-reduction protocols in quantum information sharing require highly accurate, real-time decoder outputs and syndrome data, as well as sufficiently fast classical processing to meet adaptive measurement loop timing constraints.

A plausible implication is that as systems further integrate cross-domain, cryptographically protected, and uncertainty-quantified data, the precise specification, analysis, and deployment of soft information sharing protocols will become an increasingly central technical concern across secure communications, distributed systems, quantum computing, and sensor networks.