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Abstract

Hierarchies arise in the context of access control whenever the user population can be modeled as a set of partially ordered classes (represented as a directed graph). A user with access privileges for a class obtains access to objects stored at that class and all descendant classes in the hierarchy. The problem of key management for such hierarchies then consists of assigning a key to each class in the hierarchy so that keys for descendant classes can be obtained via efficient key derivation. We propose a solution to this problem with the following properties: (1) the space complexity of the public information is the same as that of storing the hierarchy; (2) the private information at a class consists of a single key associated with that class; (3) updates (i.e., revocations and additions) are handled locally in the hierarchy; (4) the scheme is provably secure against collusion; and (5) each node can derive the key of any of its descendant with a number of symmetric-key operations bounded by the length of the path between the nodes. Whereas many previous schemes had some of these properties, ours is the first that satisfies all of them. The security of our scheme is based on pseudorandom functions, without reliance on the Random Oracle Model. Another substantial contribution of this work is that we are able to lower the key derivation time at the expense of modestly increasing the public storage associated with the hierarchy. Insertion of additional, so-called shortcut, edges, allows to lower the key derivation to a small constant number of steps for graphs that are total orders and trees by increasing the total number of edges by a small asymptotic factor such as O (log * n ) for an n -node hierarchy. For more general access hierarchies of dimension d , we use a technique that consists of adding dummy nodes and dimension reduction. The key derivation work for such graphs is then linear in d and the increase in the number of edges is by the factor O (log d − 1 n ) compared to the one-dimensional case. Finally, by making simple modifications to our scheme, we show how to handle extensions proposed by Crampton [2003] of the standard hierarchies to “limited depth” and reverse inheritance.

Abstract. Protection of software code against illegitimate modifications by its users is a pressing issue to many software developers. Many software-based mechanisms for protecting program code are too weak (e.g., they have single points of failure) or too expensive to apply (e.g., they incur heavy runtime performance penalty to the protected programs). In this paper, we present and explore a methodology that we believe can protect program integrity in a more tamper-resilient and flexible manner. Our approach is based on a distributed scheme, in which protection and tamper-resistance of program code is achieved, not by a single security module, but by a network of (smaller) security units that work together in the program. These security units, or guards, can be programmed to do certain tasks (checksumming the program code is one example) and a network of them can reinforce the protection of each other by creating mutual-protection. We have implemented a system for automating the process of installing guards into Win32 executables 1 . It is because our system operates on binaries that we are able to apply our protection mechanism to EXEs and DLLs. Experimental results show that memory space and runtime performance impacts incurred by guards can be kept very low (as explained later in the paper).

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We i n vestigate the outsourcing of numerical and scienti c computations using the following framework: A customer who needs computations done but lacks the computational resources computing power, appropriate software, or programming expertise to do these locally, w ould like to use an external agent to perform these computations. This currently arises in many practical situations, including the nancial services and petroleum services industries. The outsourcing is secure if it is done without revealing to the external agent either the actual data or the actual answer to the computations. The general idea is for the customer to do some carefully designed local preprocessing disguising of the problem and or data before sending it to the agent, and also some local postprocessing of the answer returned to extract the true answer. The disguise process should be as lightweight as possible, e.g., take time proportional to the size of the input and answer. The disguise preprocessing that the customer performs locally to hide" the real computation can change the numerical properties of the computation so that numerical stability m ust be considered as well as security and computational performance. We present a framework for disguising scienti c computations and discuss their costs, numerical properties, and levels of security. We show that no single disguise technique is suitable for a broad range of scienti c computations but their is an array of disguise techniques available so that almost any scienti c computation could be disguised at a reasonable cost and with very high levels of security. These disguise techniques can be embedded in a very high level, easy-to-use system problem solving environment that hides their complexity.

We give an efficient protocol for sequence comparisons of the edit-distance kind, such that neither party reveals anything about their private sequence to the other party (other than what can be inferred from the edit distance between their two sequences -which is unavoidable because computing that distance is the purpose of the protocol). The amount of communication done by our protocol is proportional to the time complexity of the best-known algorithm for performing the sequence comparison.The problem of determining the similarity between two sequences arises in a large number of applications, in particular in bioinformatics. In these application areas, the edit distance is one of the most widely used notions of sequence similarity: It is the least-cost set of insertions, deletions, and substitutions required to transform one string into the other. The generalizations of edit distance that are solved by the same kind of dynamic programming recurrence relation as the one for edit distance, cover an even wider domain of applications.