A SEMANTIC BASED POLICY MANAGEMENT FRAMEWORK FOR CLOUD COMPUTING ENVIRONMENTS

Cloud computing paradigm has gained tremendous momentum and generated intensive interest. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this dissertation, we mainly focus on issues related to policy management and access control in the cloud. Currently, users have to use diverse access control mechanisms to protect their data when stored on the cloud service providers (CSPs). Access control policies may be specified in different policy languages and heterogeneity of access policies pose significant problems.An ideal policy management system should be able to work with all data regardless of where they are stored. Semantic Web technologies when used for policy management, can help address the crucial issues of interoperability of heterogeneous CSPs. In this dissertation, we propose a semantic based policy management framework for cloud computing environments which consists of two main components, namely policy management and specification component and policy evolution component. In the policy management and specification component, we first introduce policy management as a service (PMaaS), a cloud based policy management framework that give cloud users a unified control point for specifying authorization policies, regardless of where the data is stored. Then, we present semantic based policy management framework which enables users to specify access control policies using semantic web technologies and helps address heterogeneity issues of cloud computing environments. We also model temporal constraints and restrictions in GTRBAC using OWL and show how ontologies can be used to specify temporal constraints. We present a proof of concept implementation of the proposed framework and provide some performance evaluation. In the policy evolution component, we propose to use role mining techniques to deal with policy evolution issues and present StateMiner, a heuristic algorithm to find an RBAC state as close as possible to both the deployed RBAC state and the optimal state. We also implement the proposed algorithm and perform some experiments to demonstrate its effectiveness

Forum Session at the First International Conference on Service Oriented Computing (ICSOC03)

The First International Conference on Service Oriented Computing (ICSOC) was held in Trento, December 15-18, 2003. The focus of the conference ---Service Oriented Computing (SOC)--- is the new emerging paradigm for distributed computing and e-business processing that has evolved from object-oriented and component computing to enable building agile networks of collaborating business applications distributed within and across organizational boundaries. Of the 181 papers submitted to the ICSOC conference, 10 were selected for the forum session which took place on December the 16th, 2003. The papers were chosen based on their technical quality, originality, relevance to SOC and for their nature of being best suited for a poster presentation or a demonstration. This technical report contains the 10 papers presented during the forum session at the ICSOC conference. In particular, the last two papers in the report ere submitted as industrial papers

WebWave: Globally Load Balanced Fully Distributed Caching of Hot Published Documents

Document publication service over such a large network as the Internet challenges us to harness available server and network resources to meet fast growing demand. In this paper, we show that large-scale dynamic caching can be employed to globally minimize server idle time, and hence maximize the aggregate server throughput of the whole service. To be efficient, scalable and robust, a successful caching mechanism must have three properties: (1) maximize the global throughput of the system, (2) find cache copies without recourse to a directory service, or to a discovery protocol, and (3) be completely distributed in the sense of operating only on the basis of local information. In this paper, we develop a precise definition, which we call tree load-balance (TLB), of what it means for a mechanism to satisfy these three goals. We present an algorithm that computes TLB off-line, and a distributed protocol that induces a load distribution that converges quickly to a TLB one. Both algorithms place cache copies of immutable documents, on the routing tree that connects the cached document's home server to its clients, thus enabling requests to stumble on cache copies en route to the home server.Harvard University; The Saudi Cultural Mission to the U.S.A

SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search

The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential. SANNS comprises two protocols: an optimized linear scan and a protocol based on a novel sublinear time clustering-based algorithm. We prove the security of both protocols in the standard semi-honest model. The protocols are built upon several state-of-the-art cryptographic primitives such as lattice-based additively homomorphic encryption, distributed oblivious RAM, and garbled circuits. We provide several contributions to each of these primitives which are applicable to other secure computation tasks. Both of our protocols rely on a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from $O(n + k^2)$ comparators. We have implemented our proposed system and performed extensive experimental results on four datasets in two different computation environments, demonstrating more than $18-31\times$ faster response time compared to optimally implemented protocols from the prior work. Moreover, SANNS is the first work that scales to the database of 10 million entries, pushing the limit by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202