Automated Synthesis of Distributed Self-Stabilizing Protocols

In this paper, we introduce an SMT-based method that automatically synthesizes a distributed self-stabilizing protocol from a given high-level specification and network topology. Unlike existing approaches, where synthesis algorithms require the explicit description of the set of legitimate states, our technique only needs the temporal behavior of the protocol. We extend our approach to synthesize ideal-stabilizing protocols, where every state is legitimate. We also extend our technique to synthesize monotonic-stabilizing protocols, where during recovery, each process can execute an most once one action. Our proposed methods are fully implemented and we report successful synthesis of well-known protocols such as Dijkstra's token ring, a self-stabilizing version of Raymond's mutual exclusion algorithm, ideal-stabilizing leader election and local mutual exclusion, as well as monotonic-stabilizing maximal independent set and distributed Grundy coloring

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data

Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Intruder deducibility constraints with negation. Decidability and application to secured service compositions

The problem of finding a mediator to compose secured services has been reduced in our former work to the problem of solving deducibility constraints similar to those employed for cryptographic protocol analysis. We extend in this paper the mediator synthesis procedure by a construction for expressing that some data is not accessible to the mediator. Then we give a decision procedure for verifying that a mediator satisfying this non-disclosure policy can be effectively synthesized. This procedure has been implemented in CL-AtSe, our protocol analysis tool. The procedure extends constraint solving for cryptographic protocol analysis in a significative way as it is able to handle negative deducibility constraints without restriction. In particular it applies to all subterm convergent theories and therefore covers several interesting theories in formal security analysis including encryption, hashing, signature and pairing.Comment: (2012

Interoperability, Trust Based Information Sharing Protocol and Security: Digital Government Key Issues

Improved interoperability between public and private organizations is of key significance to make digital government newest triumphant. Digital Government interoperability, information sharing protocol and security are measured the key issue for achieving a refined stage of digital government. Flawless interoperability is essential to share the information between diverse and merely dispersed organisations in several network environments by using computer based tools. Digital government must ensure security for its information systems, including computers and networks for providing better service to the citizens. Governments around the world are increasingly revolving to information sharing and integration for solving problems in programs and policy areas. Evils of global worry such as syndrome discovery and manage, terror campaign, immigration and border control, prohibited drug trafficking, and more demand information sharing, harmonization and cooperation amid government agencies within a country and across national borders. A number of daunting challenges survive to the progress of an efficient information sharing protocol. A secure and trusted information-sharing protocol is required to enable users to interact and share information easily and perfectly across many diverse networks and databases globally.Comment: 20 page

Masquerade attack detection through observation planning for multi-robot systems

The increasing adoption of autonomous mobile robots comes with a rising concern over the security of these systems. In this work, we examine the dangers that an adversary could pose in a multi-agent robot system. We show that conventional multi-agent plans are vulnerable to strong attackers masquerading as a properly functioning agent. We propose a novel technique to incorporate attack detection into the multi-agent path-finding problem through the simultaneous synthesis of observation plans. We show that by specially crafting the multi-agent plan, the induced inter-agent observations can provide introspective monitoring guarantees; we achieve guarantees that any adversarial agent that plans to break the system-wide security specification must necessarily violate the induced observation plan.Accepted manuscrip