Ad hoc networks may not be suitable for “non ad hoc” applications due to resource, mobility, traffic pattern and incompatible wireless MAC protocols issues. We propose the Hierarchical Mobile Wireless Network for providing flexible and scalable network services to these applications. In such a system, mobile hosts are organized into hierarchical groups. Four basic operations that are used to set up and maintain the network structure are described. An efficient protocol for group membership management is discussed. The Segmented Membership-based Group Routing protocol is presented. In this routing protocol, only local message exchanging is required. Simulation-based experiments confirm the scalability of our design.

Digital Watermarking, in the traditional sense is the technique of embedding un-detectable (un-perceivable) hidden information into multimedia objects (i.e. images, audio, video, text) mainly to protect the data from unauthorized duplication and distribution by enabling provable ownership over the content.
  Recent research of the authors introduces the issue of digital watermarking for generic number sets. In the present paper we expand on this foundation and introduce a solution for relational database content security through watermarking. To the best of our knowledge there is no research on this issue. Our solution addresses a series of important attacks, such as data re-sorting, subset selection (up to 30% and above data loss tolerance), linear data changes. Finally we present dbwm.*, a proof-of-concept implementation of our algorithm and its application on real life data, namely in watermarking data from the outsourced Wal-Mart sales database of the years 1999-2000.

A good direction towards building secure systems that operate efficiently in large-scale environments (like the World Wide Web) is the deployment of Role Based Access Control Methods (RBAC). RBAC architectures do not deal with each user separately, but with discrete roles that users can acquire in the system. The goal of this paper is to present a classification algorithm that during its training phase, classifies roles of the users in clusters. The behavior of each user that enters the system holding a specific role is traced via audit trails and any misbehavior is detected and reported (classification phase). This algorithm will be incorporated in the Role Server architecture, currently under development, enhancing its ability to dynamically adjust the amount of trust of each user and update the corresponding role assignments.

This paper describes the design of a censorship-resistant distributed file sharing protocol which has been implemented on top of GNUnet, an anonymous, reputation-based network. We focus on the encoding layer of the GNUnet file-sharing protocol which supports efficient dissemination of encrypted data as well as queries over encrypted data. The main idea advocated in this paper is that simple cryptographic techniques are sufficient to engineer an efficient data encoding that can make it significantly harder to selectively censor information. Our encoding allows users to share files encrypted under descriptive keys which are the basis for querying the network for content. A key property of our encoding is that intermediaries can filter invalid encrypted replies without being able to decrypt the query or the reply. Files are stored in small chunks which are distributed and replicated automatically by the GNUnet infrastructure. Additionally, data files may be stored in plaintext or encrypted form or as a combination of both and encrypted on demand.

Given two strings T = a 1 : : :a n and P = b 1 : : :b m over an alphabet , the problem of testing whether P occurs as a subsequence of T is trivially solved in linear time. It is also known that a simple O ( n log j j) time preprocessing of T makes it easy to decide subsequently for any P and in at most j P j log j j character comparisons, whether P is a subsequence of T . These problems become more complicated if one asks instead whether P occurs as a subsequence of some substring Y of T of bounded length. This paper presents an automaton built on the textstring T and capable of identifying all distinct minimal substrings Y of X having P as a subsequence. By a substring Y being minimal with respect to P , it is meant that P is not a subsequence of any proper substring of Y . For every minimal substring Y , the automaton recognizes the occurrence of P having lexicographically smallest sequence of symbol positions in Y . It is not di cult to realize such an automaton in time and space O ( n 2
) for a text of n characters. One result of this paper consists of bringing those bounds down to linear or O ( n log n ), respectively, depending on whether the alphabet is bounded or of arbitrary size, thereby matching the respective complexities of o -line exact string searching. Having built the automaton, the search for all lexicographically earliest occurrences of P in X is carried out in time O ( n + P m
i=1 rocc i   i   log n   log j j), where rocc i is the number of distinct minimal substrings of T having b 1 : : :b i as a subsequence. All log factors appearing in the above bounds can be further reduced to log log by resort to known integer-handling data structures.

We show that matrix multiplication, matrix inversion, convolution, and sorting can be securely “outsourced”, in the following sence: A customer who needs these computations done on some data but lacks the computational resources (or programming expertise) to do so, can use an external agent to perform these computations without revealing to the agent either the actual data or the actual answer to the computation. This general situation currentlly arises in many practical situations, including the finacial services and petroleum services industries. The general idea is for the customer to do some carefully designed local preprocessing of data before sending it to the agent, and also some local postprocessing of the answer returned by the agent, in order to extract from it the true answer. The pre- and post- processing should not take more time than proportional to the size of the input, which is unavoidable because the customer must at least read the input once. The purpose of the preprocessing step that the customer performs locally is to “hide” the real data The purpose of the postprocessing is to extract from the noisy answer returned by the agent the true answer that the customer seeks.

In a computer network that consists of M subnetworks, the L-bit address of a machine consists of two parts: A prefix s(sub i) that contains the address of the subnetwork to which the machine belongs, and a sufix (of length L - |s(sub i)| ) containing the address of that particular machine within its subnetwork. In fixed-length subnetwork addressing, |s(sub i)| varies from one subnetwork to another. To avoid ambiguity when decoding addresses, there is a requirement that no s(sub i) be a prefix of another s(sub j). The practical problem is how to find a suitable set of s(sub i’s) in order to maximize the total number of addressable machines, when the ith subnetwork contains n(sub i) machines. Not all of the n(sub i) machines of a subnetwork i need to be addressable in a solution: If n(sub i) > 2^(L - |s(sub i)|) then only 2^(L - |s(sub m machines of that subnetwork are addressable (none is addressable if the solution assigns no address s(sub i) to that subnetwork). The abstract problem implied by this formulation is: Given an integer L, and given M (not necessarily distinct) positive integers n1,....n(sub m) find M binary stings s1, ... s(sub m) (some of which may be empty) such that (i) no nonempty string s(sub i) is prefix of another string s(sub j), (ii) no s(sub i) is more than L bits long, and (iii) the quantity (sumation |s(sub k)| is not equal to 0 ) min{n(sub k, 2^(L - |s(sub k)|} is maximized. We generalize the algorithm to the case where each n(sub i) also has a priority p(sub i) associated with it and there is an additional constraint involving priorities: Some subnetworks are then more important than others and are treated preferentially when assigning addresses. The algorithms can be used to solve the case when L itself is a variable; that is, when the input no longer specifies L but rather gives a target integer gama for the number of addressable machines, and the goal is to find the smallest L whose corresponding optimal solution results in at least gama addressable machines.

We give a randomized O(N Log M) time algorithm for determining the Hamming-distance score vector between a text string T of length N and a pattern string P of length M M <= N. This is the vector whose ith entry contains the number of positions at which there is equality between the symbols of the pattern and the corresponding positions of the substring that begins at the i-th position of the text. No assumptions are made about the size of the alphabet or about the probabilistic characteristics of the input. The solution extends to the weighted case, and to higher dimensions.