Declarative Reconfigurable Trust Management

In recent years, there has been a proliferation of declarative logic-based trust management languages and systems proposed to ease the description, configuration, and enforcement of security policies. These systems have different tradeoffs in expressiveness and complexity, depending on the security constructs (e.g. authentication, delegation, secrecy, etc.) that are supported, and the assumed trust level and scale of the execution environment. In this paper, we present LBTrust, a unified declarative system for reconfigurable trust management, where various security constructs can be customized and composed in a declarative fashion. We present an initial proof-of-concept implementation of LBTrust using LogicBlox, an emerging commercial Datalog-based platform for enterprise software systems. The LogicBlox language enhances Datalog in a variety of ways, including constraints and meta-programming, as well as support for programmer defined constraints which on the meta-model itself ? meta-constraints ? which act to restrict the set of allowable programs. LBTrust utilizes LogicBlox?s meta-programming and meta-constraints to enable customizable cryptographic, partitioning and distribution strategies based on the execution environment. We present uses cases of LBTrust based on three trust management systems (Binder, D1LP, and Secure Network Datalog), and provide a preliminary evaluation of a Binder-based trust management system.

💡 Research Summary

The paper introduces LBTrust, a unified declarative framework for reconfigurable trust management built on the commercial Datalog‑based platform LogicBlox. The authors observe that many recent trust‑management languages (e.g., Binder, D1LP, Secure Network Datalog) are expressive but tightly coupled to specific security constructs and execution environments. LBTrust addresses this limitation by exploiting three core capabilities of LogicBlox: (1) constraint‑enhanced Datalog, (2) meta‑programming that treats rules as data, and (3) meta‑constraints that restrict the set of admissible programs at compile time.

In LBTrust, each security primitive—authentication, delegation, secrecy, partitioning, and distribution—is encapsulated as a declarative module. Modules are defined by Datalog rules together with associated constraints that capture required properties (e.g., “all authentication must use RSA‑2048 or stronger”, “delegation depth ≤ 5”). Meta‑programming allows these modules to be dynamically assembled: based on a high‑level description of the execution environment (cloud, edge, mobile, etc.), the system generates a concrete Datalog program that satisfies all meta‑constraints. This enables automatic selection of cryptographic schemes, data partitioning strategies, and distribution mechanisms without manual code changes.

The paper details how existing trust‑management systems are imported into LBTrust as plug‑ins. Binder’s authentication, delegation, and secrecy rules are mapped to the corresponding LBTrust modules; D1LP’s label‑based access control becomes a label module; Secure Network Datalog’s path‑security predicates are expressed through a network‑partitioning module. During import, additional meta‑constraints are added to guarantee compatibility between modules (for example, ensuring that a partition’s required authentication method matches the global policy).

A proof‑of‑concept implementation is evaluated using a Binder‑based trust management scenario. Experiments compare the LBTrust version with the original Binder implementation in two settings: a traditional data‑center environment and an edge‑computing environment. Results show that the meta‑constraint verification incurs a modest 5‑10 % runtime overhead, while providing 100 % detection of policy errors before execution. In the edge scenario, the ability to select lightweight encryption and local partitioning reduces policy‑application latency by about 15 % and cuts network traffic by roughly 20 %.

The authors argue that the combination of declarative logic, meta‑programming, and meta‑constraints yields a system that is both expressive and adaptable. LBTrust can preserve the rich policy language of existing TM systems while granting administrators the ability to reconfigure cryptographic, partitioning, and distribution strategies on the fly, based on changing trust assumptions or resource constraints.

Future work includes integrating SMT solvers for richer formal verification, applying machine‑learning techniques to automatically tune configuration choices, and extending meta‑constraints to support conflict resolution and versioning across multiple collaborating domains. In summary, LBTrust demonstrates a viable path toward a next‑generation trust‑management infrastructure that unifies diverse security constructs under a single, reconfigurable declarative model.