In this paper, we explore a new form of dissemination that arises in distributed, mission-critical applications called Flash Dissemination. This involves the rapid dissemination of rich information to a large number of recipients in a very short period of time. A key characteristic of Flash Dissemination is its unpredictability (e.g., natural hazards), but when invoked it must harness all possible resources to ensure timely delivery of information. Additionally, it must scale to a large number of recipients and perform efficiently in highly heterogeneous (data, network) and failure prone environments. We investigate a peer-based approach based on the simple principle of transferring dissemination load to information receivers using foundations from broadcast networks, gossip theory, and random networks. Gossip-based protocols are well known for being stateless, scalable, and fault-tolerant; however, their performance degrades as content size increases, because of the propagation of redundant gossip messages. In this paper, we propose Concurrent Random Expanding Walkers (CREW), a smart gossip protocol designed to maximize the speed of dissemination by transmitting data only as needed, and by exploiting both intra- and internode concurrency. CREW is designed to support both content and network heterogeneity and deal with transmission failures without sacrificing dissemination speed. We implemented CREW on top of a scalable middleware environment that allows for deployment across several platforms and developed optimizations without compromising on the stateless nature of CREW. We evaluated CREW empirically and compared it to optimized implementations of popular gossip and peer-based systems. Our experiments show that CREW significantly outperforms both traditional gossip and current large content dissemination systems while sustaining its performance in the presence of network errors.

This paper describes the CloudProtect middleware that empowers users to encrypt sensitive data stored within various cloud applications. However, most web applications require data in plaintext for implementing the various functionalities and in general, do not support encrypted data management. Therefore, CloudProtect strives to carry out the data transformations (encryption/decryption) in a manner that is transparent to the application, i.e., preserves all functionalities of the application, including those that require data to be in plaintext. Additionally, CloudProtect allows users flexibility in trading off performance for security in order to let them optimally balance their privacy needs and usage-experience.

This paper explores an efficient and secure mechanism to partition computations across public and private machines in a hybrid cloud setting. We propose a principled framework for distributing data and processing in a hybrid cloud that meets the conflicting goals of performance, sensitive data disclosure risk and resource allocation costs. The proposed solution is implemented as an add-on tool for a Hadoop and Hive based cloud computing infrastructure. Our experiments demonstrate that the developed mechanism can lead to a major performance gain by exploiting both the hybrid cloud components without violating any pre-determined public cloud usage constraints.

In this paper, we propose a content-based publish/subscribe (pub/sub) framework that delivers matching content to subscribers in their desired format. Such a framework enables the pub/sub system to accommodate richer content formats including multimedia publications with image and video content. In our proposed framework, users (consumers) in addition to specifying their information needs (subscription queries), also specify their profile which includes the information about their receiving context which includes characteristics of the device used to receive the content (e.g., resolution of a PDA used by a consumer). The pub/sub system besides being responsible for matching and routing the published content, also becomes responsible for converting the content into the suitable format for each user. Content conversion is achieved through a set of content adaptation operators (e.g., image transcoder, document translator, etc.). We study algorithms for placement of such operators in heterogeneous pub/sub broker overlay in order to minimize the communication and computation resource consumption. Our experimental results show that careful placement of operators in pub/sub overlay network results in significant cost reduction.

Many DBMS products in the market provide built in encryption support to deal with the security concerns of the organizations. This solution is quite effective in preventing data leakage from compromised/stolen storage devices. However, recent studies show that a significant part of the leaked records have been done so by using specialized malwares that can access the main memory of systems. These malwares can easily capture the sensitive information that are decrypted in the memory including the cryptographic keys used to decrypt them. This can further compromise the security of data residing on disk that are encrypted with the same keys. In this paper we quantify the disclosure risk of encrypted data in a relational DBMS for main memory-based attacks and propose modifications to the standard query processing mechanism to minimize such risks. Specifically, we propose query optimization techniques and disclosure models to design a data-sensitivity aware query optimizer. We implemented a prototype DBMS by modifying both the storage engine and optimizer of MySQL-InnoDB server. The experimental results show that the disclosure risk of such attacks can be reduced dramatically while incurring a small performance overhead in most cases.

In this paper, we consider privacy challenges in event-driven pervasive spaces where multimedia streams captured by sensors embedded in the infrastructure are used to detect a variety of application-specific media events. In particular, we develop techniques to detect events without disclosing any identifying information unless necessary. We characterize the nature of inference channels that arise and model privacy preserving event detection as an optimization problem that attempts to balance disclosure with performance. We design and test efficient communication protocols that realize this tradeoff.

Surveillance is an important task for guaranteeing the security of individuals. Being able to intelligently monitor the activity in given spaces is essential to achieve such surveillance. Sentient spaces based on a large set of sensors provide the potential for such intelligent monitoring. However, heavily instrumenting a space with sensors it is not enough to build a sentient space. One needs a software architecture that allows programming all these sensors in a transparent and efficient manner. In this paper, we present SATware, a stream acquisition and transformation middleware we are developing to analyze, query, and transform multimodal sensor data streams to facilitate flexible development of sentient environments. SATware provides a powerful application development environment in which users (i.e., application builders) can focus on the specifics of the application without having to deal with the technical peculiarities of accessing a large number of diverse sensors via different protocols.

k-anonymity is a popular measure of privacy for data publishing: It measures the risk of identity-disclosure of individuals whose personal information are released in the form of published data for statistical analysis and data mining purposes(e.g. census data). Higher values of k denote higher level of privacy (smaller risk of disclosure). Existing techniques to achieve k-anonymity use a variety of “generalization” and “suppression” of cell values for multi-attribute data. At the same time, the released data needs to be as “information-rich” as possible to maximize its utility. Information loss becomes an even greater concern as more stringent privacy constraints are imposed [4]. The resulting optimization problems have proven to be computationally intensive for data sets with large attribute-domains. In this paper, we develop a systematic enumeration based branch-and-bound technique that explores a much richer space of solutions than any previous method in literature. We further enhance the basic algorithm to incorporate heuristics that potentially accelerate the search process significantly.

Location-based information contained in publicly available GIS databases is invaluable for many applications such as disaster response, national infrastructure protection, crime analysis, and numerous others. The information entities of such databases have both spatial and textual descriptions. Likewise, queries issued to the databases also contain spatial and textual components, for example, "Find shelters with emergency medical facilities in Orange County," or "Find earthquake-prone zones in Southern California." We refer to such queries as spatial-keyword queries or SK queries for short. In recent times, a lot of interest has been generated in efficient processing of SK queries for a variety of applications from Web-search to GIS decision support systems. We refer to systems built for enabling such applications as Geographic Information Retrieval (GIR) Systems. An example GIR system that we address in this paper is a search engine built on top of hundreds of thousands of publicly available GIS databases. Building a search engine over such large repositories is a challenge. One of the key aspects of such a search engine is the performance. In this paper, we propose a framework for GIR systems and focus on indexing strategies that can process SK queries efficiently. We show through experiments that our indexing strategies lead to significant improvement in efficiency of answering SK queries over existing techniques.

In this paper, we explore a new form of dissemination called Flash Dissemination that involves dissemination of fixed, rich information to a large number of recipients in as short a time as possible. Key characteristics of Flash Dissemination include unpredictability in its need, scalability to large number of recipients and autonomic performance in highly heterogenous and failureprone environments. Previous work either addresses large content delivery in heterogenous networks or fault-tolerant dissemination of (streaming) events. We investigate a peer-based approach using foundations from broadcast networks, gossip theory and random networks. In this paper, we propose CREW (Concurrent Random Expanding Walkers), a scalable, lightweight, and autonomic gossip-based protocol. CREW is also explicitly designed to maximize the speed of dissemination using adaptive and intelligent intra and inter node concurrency. We implemented CREW on top of a scalable middleware environment and compared it to optimized implementations of popular gossip and peer-based systems. Our experiments show that CREW outperforms both traditional gossip and current large content dissemination systems, across a wide range of comparative metrics, even though its design is counterintuitive from a systems perspective.

An important class of queries is the LIKE predicate in SQL. In the absence of an index, LIKE queries are subject to performance degradation. The notion of indexing on substrings (or q-grams) has been explored earlier without sufficient consideration of efficiency. q-grams are used to prune away rows that do not qualify for the query. The problem is to identify a finite number of grams subject to storage constraint that gives maximal pruning for a given query workload. Our contributions include: i) a formal problem definition, that produces results within a provable error bound, ii) performance evaluation of the application of the novel method to real data, and iii) parallelization of the algorithm, scaling considerations and a proposal to handle scaling issues.