Gasping for AIR Why we need Linked Rules and Justifications on the Semantic Web

The Semantic Web is a distributed model for publishing, utilizing and extending structured information using Web protocols. One of the main goals of this technology is to automate the retrieval and integration of data and to enable the inference of interesting results. This automation requires logics and rule languages that make inferences, choose courses of action, and answer questions. The openness of the Web, however, leads to several issues including the handling of inconsistencies, integration of diverse information, and the determination of the quality and trustworthiness of the data. AIR is a Semantic Web-based rule language that provides this functionality while focusing on generating and tracking explanations for its inferences and actions as well as conforming to Linked Data principles. AIR supports Linked Rules, which allow rules to be combined, re-used and extended in a manner similar to Linked Data. Additionally, AIR explanations themselves are Semantic Web data so they can be used for further reasoning. In this paper we present an overview of AIR, discuss its potential as a Web rule language by providing examples of how its features can be leveraged for different inference requirements, and describe how justifications are represented and generated.This material is based upon work supported by the National Science Foundation under Award No. CNS-0831442, by the Air Force Office of Scientific Research under Award No. FA9550-09-1-0152, and by Intelligence Advanced Research Projects Activity under Award No. FA8750-07-2-0031

DPD-InfoGAN: Differentially Private Distributed InfoGAN

Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced. Due to the high model complexity of InfoGAN, the generative distribution tends to be concentrated around the training data points. This is a critical problem as the models may inadvertently expose the sensitive and private information present in the dataset. To address this problem, we propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacy-preserving manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can synthesize high-quality images with flexible control over image attributes while preserving privacy

On Security in Open Multi-Agent Systems

In open multi-agent systems agents must interact with other agents with which they are not familiar. In particular, an agent will receive requests and assertions from other agents and must decide how to act on the requests and assess the credibility of the assertions. In a closed environment, agents have well known and familiar transaction partners whose rights and credibility are known. The problem thus reduces to authentication which is the reliable identification of agents' true identity. In an open environment, however, agents must transact business even when knowing the true identities is uninformative. Decisions about who to believe and who to serve must be based on an agent's properties. These properties are established by proving them from an agent's credentials, beliefs of other agents and the appropriate security policies. In this paper we present an approach to some security problems in open multi-agent systems based on distributed trust, the delegation of permissions and distributed belief. Distributed trust management involves verifying if the requesting agent meets the required credentials for the request. As distributed trust management is inherently policy-based, our approach also includes a flexible policy language. Agents collect information that they require to make security decisions through distributed belief, which allows rules to be specified for accepting beliefs of other agents. We begin by describing our approach and the concepts on which it is built. Then we present a design that provides security functionality in a typical agent framework (FIPA) and describe initial work in its realization

Rei: A policy language for the me-centric project

security, me-centric, policy, deontic Policies guide the way entities within a domain act, by providing rules for their behavior. Most of the research in policies is within a certain application area, for example security for databases, and there are no general specifications for policies. Another problem with policies is that they require domain dependent information, forcing researchers to create policy languages that are bound to the domains for which they were developed. This prevents policy languages from being flexible and being usable across domains. This report describes the specifications of the Rei policy language, which provides constructs based on deontic concepts. These constructs are extremely flexible and allow different kinds of policies to be stated. This simple policy language is not tied to any specific application and allows domain dependent information to be added without any modification. The policy engine associated with Rei accepts policies in first order logic and RDF. The report also discusses the functionality of the policy engine that interprets and reasons over Rei policies