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Online negative databases
 PROCEEDINGS OF THIRD INTERNATIONAL CONFERENCE ON ARTIFICIAL IMMUNE SYSTEMS (ICARIS 2004), PAGES 175 – 188
, 2004
"... The benefits of negative detection for obscuring information are explored in the context of Artificial Immune Systems (AIS). AIS based on string matching have the potential for an extra security feature in which the “normal” profile of a system is hidden from its possible hijackers. Even if the mode ..."
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Cited by 16 (7 self)
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The benefits of negative detection for obscuring information are explored in the context of Artificial Immune Systems (AIS). AIS based on string matching have the potential for an extra security feature in which the “normal” profile of a system is hidden from its possible hijackers. Even if the model of normal behavior falls into the wrong hands, reconstructing the set of valid or “normal” strings is an N Phard problem. The datahiding aspects of negative detection are explored in the context of an application to negative databases. Previous work is reviewed describing possible representations and reversibility properties for privacyenhancing negative databases. New algorithms are described, which allow online creation and updates of negative databases, and future challenges are discussed.
Learning to create is as hard as learning to appreciate
, 2010
"... We explore the relationship between a natural notion of “learning to create ” (LTC) studied by Kearns et al. (STOC ’94) and the standard PAC model of Valiant (CACM ’84), which can be thought of as a formalization of “learning to appreciate”. Our main theorem states that “if learning to appreciate is ..."
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We explore the relationship between a natural notion of “learning to create ” (LTC) studied by Kearns et al. (STOC ’94) and the standard PAC model of Valiant (CACM ’84), which can be thought of as a formalization of “learning to appreciate”. Our main theorem states that “if learning to appreciate is hard, then so is learning to create”. More formally, we prove that if there exists a concept class for which PAC learning with respect to efficiently samplable input distributions is hard, then there exists another (possibly richer) concept class for which the LTC problem is hard. We also show that our theorem is tight in two senses, by proving that there exist concrete concept classes for which PAC learning is hard but LTC is easy, and by showing that it is unlikely our main theorem can be improved to the case of PAC learning with respect to unsamplable input distributions. 1
On Invertible Sampling and Adaptive Security
"... Abstract Secure multiparty computation (MPC) is one of the most general and well studied problems in cryptography. We focus on MPC protocols that are required to be secure even when the adversary can adaptively corrupt parties during the protocol, and under the assumption that honest parties cannot ..."
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Abstract Secure multiparty computation (MPC) is one of the most general and well studied problems in cryptography. We focus on MPC protocols that are required to be secure even when the adversary can adaptively corrupt parties during the protocol, and under the assumption that honest parties cannot reliably erase their secrets prior to corruption. Previous feasibility results for adaptively secure MPC in this setting applied either to deterministic functionalities or to randomized functionalities which satisfy a certain technical requirement. The question whether adaptive security is possible for all functionalities was left open. We provide the first convincing evidence that the answer to this question is negative, namely that some (randomized) functionalities cannot be realized with adaptive security. We obtain this result by studying the following related invertible sampling problem: given an efficient sampling algorithm A, obtain another sampling algorithm B such that the output of B is computationally indistinguishable from the output of A, but B can be efficiently inverted (even if A cannot). This invertible sampling problem is independently motivated by other cryptographic applications. We show, under strong but well studied assumptions, that there exist efficient sampling algorithms A for which invertible sampling as above is impossible. At the same time, we show that a general feasibility result for adaptively secure MPC implies that invertible sampling is possible for every A, thereby reaching a contradiction and establishing our main negative result. 1
• Mikhail Belkin (Ohio State University)
"... Copyright © 2010 These materials are owned by their respective copyright owners. All rights reserved. ISBN number: 9780982252925 ..."
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Copyright © 2010 These materials are owned by their respective copyright owners. All rights reserved. ISBN number: 9780982252925
International Journal of Information Security manuscript No. (will be inserted by the editor)
"... Information Abstract In a negative representation a set of elements (the positive representation) is depicted by its complement set. That is, the elements in the positive representation are not explicitly stored, and those in the negative representation are. The concept, feasibility, and properties ..."
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Information Abstract In a negative representation a set of elements (the positive representation) is depicted by its complement set. That is, the elements in the positive representation are not explicitly stored, and those in the negative representation are. The concept, feasibility, and properties of negative representations are explored in the paper; in particular, its potential to address privacy concerns. It is shown that a positive representation consisting of n lbit strings can be represented negatively using only O(ln) strings, through the use of an additional symbol. It is also shown that membership queries for the positive representation can be processed against the negative representation in time no worse than linear in its size, while reconstructing the original positive set from its negative representation is an N Phard problem.