18303958
Self-healing key distribution with revocation
We address the problem of establishing a group key amongst a dynamic group of users over an unreliable, or lossy, network. We term our key distribution mechanisms self-healing because users are capable of recovering lost group keys on their own, without requesting additional transmissions from the group manager thus cutting back on network traffic, decreasing the load on the group manager and reducing the risk of user exposure through traffic analysis. A user must be a member both before and after the session in which a particular key is sent in order to be able to recover the key through self-healing. Binding the ability to recover keys to membership status enables the group manager to use short broadcasts to establish group keys, independent of the group size. In addition, the self-healing approach to key distribution is stateless, meaning that a group member who has been off-line for some time is able to recover new session keys immediately after coming back on-line.
3112017
Distribution chain security
Digital content distribution systems will enable business models in the near future that cannot be predicted today. In this paper, we identify a new security problem that can be crucial to this enablement. The problem arises from the con icting privacy and integrity goals of middlemen in digital distribution chains. Our solution is a novel system design that incorporates obfuscated digital contracts, semi-trusted contract certi ers, and zero-knowledge proofs of arithmetic relations. Our implementation and timing experiments demonstrate that our solution is practical and eÆcient.
2563843
An algebraic approach to IP traceback
We present a new solution to the problem of determining the path a packet traversed over the Internet (called the traceback problem) during a denial-of-service attack. This article reframes the traceback problem as a polynomial reconstruction problem and uses algebraic techniques from coding theory and learning theory to provide robust methods of transmission and reconstruction.
14470215
Genetic epidemiology, parallel algorithms, and workstation networks
Many interesting problems in genetic epidemiology are formulated as non-linear optimization problems using the Gemini/Almini library of routines. Because of the wide availability of networked workstations, we investigate cost-effectively improving the performance of the Gemini/Almini library by exploiting parallelism with a set of workstations connected via a local area network. Instrumentation of the Gemini/Almini optimization routines reveals significant potential for improving performance via parallelism. Using these instrumentation results, we identify promising targets of parallelism and discuss two preliminary implementations that demonstrate the potential benefits of cost-effective parallel implementations. By applying parallelism to the Almini/Gemini routines, we hope to potentially improve the performance of a large number of genetic epidemiological applications.<<ETX>>
7064637
Challenges in Ubiquitous Data Management
Ubiquitous computing is a compelling vision for the future that is moving closer to realization at an accelerating pace. The combination of global wireless and wired connectivity along with increasingly small and powerful devices enables a wide array of new applications that will change the nature of computing. Beyond new devices and communications mechanisms, however, the key technology that is required to make ubiquitous computing a reality is data management. In this short paper, I attempt to identify and organize the key aspects of ubiquitous computing applications and environments from a data management perspective and outline the data management challenges that they engender. Finally, I describe two on-going projects: Data Recharging and Telegraph, that are addressing some of these issues.
14137960
Scheduling for large-scale on-demand data broadcasting
Advances in telecommunications have enabled the deployment of broadcast-based wide-area information services that provide on-demand data access to very large client populations. In order to effectively utilize a broadcast medium for such a service, it is necessary to have efficient, on-line scheduling algorithms that can balance individual and overall performance and can scale in terms of data set sizes, client populations, and broadcast bandwidth. In this study we introduce a parameterized algorithm that provides good performance across all of these criteria and can be tuned to emphasize either average or worst case waiting time. Unlike previous work on low overhead scheduling, the algorithm is not based on estimates of the access probabilities of items, but rather, it makes scheduling decisions based on the current queue state, allowing it to easily adapt to changes in the intensity and distribution of the workload. We examine the performance of the algorithm using a simulation model.
270075
Adaptive execution of variable-accuracy functions
Many analysis applications require the ability to repeatedly execute sophisticated modeling functions, which can each take minutes or even hours to produce a single answer. Because of this expense, such applications have largely been unable to directly use such models in queries, with either on-demand or continuous query processing technology. Query processors are hindered in their ability to optimize expensive modeling functions due to the "black box" nature of existing user-defined function (UDF) interfaces. In this paper, we address the problem of querying over sophisticated models with the development of VAOs (Variable-Accuracy Operators). VAOs use a new function interface that exposes the trade-off between compute time and accuracy that exists in many modeling functions. Using this interface, VAOs adaptively run each function call in a query only to an accuracy needed to answer the query, thus eliminating unneeded work. In this paper, we present the design of VAOs for a set of common query operations. We show the effectiveness of VAOs using a prototype implementation running financial queries over real bond market data.
10278200
Efficient Incremental Garbage Collection for Client-Server Object Database Systems
We briefly outline the main characteristics of an efficient server-based algorithm for garbage collecting object-oriented databases in a client-server environment. The algorithm is incremental and runs concurrently with client transactions. Unlike previous algorithms, it does not hold any locks on data and does not require callbacks to clients. It is fault tolerant, but performs very little logging. The algorithm has been designed to be integrated into existing OODB systems, and therefore it works with standard implementation techniques such as two-phase locking and write-ahead-logging. In addition, it supports client-server performance optimizations such as client caching and flexible management of client buffers. The algorithm has been implemented in the EXODUS storage manager before being evaluated.