Abstract. In 1980 Martin Hellman described a cryptanalytic time-memory trade-off which reduces the time of cryptanalysis by using precalculated data stored in memory. This technique was improved by Rivest before 1982 with the introduction of distinguished points which drastically reduces the number of memory lookups during cryptanalysis. This improved technique has been studied extensively but no new optimisations have been published ever since. We propose a new way of precalculating the data which reduces by two the number of calculations needed during cryptanalysis. Moreover, since the method does not make use of distinguished points, it reduces the overhead due to the variable chain length, which again significantly reduces the number of calculations. As an example we have implemented an attack on MS-Windows password hashes. Using 1.4GB of data (two CD-ROMs) we can crack 99.9% of all alphanumerical passwords hashes (2 37 ) in 13.6 seconds whereas it takes 101 seconds with the current approach using distinguished points. We show that the gain could be even much higher depending on the parameters used.
The biggest challenge for RFID technology is to provide benefits without threatening the privacy of consumers. Many solutions have been suggested but almost as many ways have been found to break them. An approach by Ohkubo, Suzuki and Kinoshita using an internal refreshment mechanism seems to protect privacy well but is not scalable. We introduce a specific time-memory trade-off that removes the scalability issue of this scheme. Additionally we prove that the system truly offers privacy and even forward privacy. Our third contribution is an extension of the scheme which offers a secure communication channel between RFID tags and their owner using building blocks that are already available on the tag. Finally we give a typical example of use of our system and show its feasibility by calculating all the parameters.
Abstract. Radio frequency identification systems based on low-cost computing devices is the new plaything that every company would like to adopt. Its goal can be either to improve the productivity or to strengthen the security. Specific identification protocols based on symmetric challengeresponse have been developed in order to assure the privacy of the device bearers. Although these protocols fit the devices' constraints, they always suffer from a large time complexity. Existing protocols require O(n) cryptographic operations to identify one device among n. Molnar and Wagner suggested a method to reduce this complexity to O(log n). We show that their technique could degrade the privacy if the attacker has the possibility to tamper with at least one device. Because low-cost devices are not tamper-resistant, such an attack could be feasible. We give a detailed analysis of their protocol and evaluate the threat. Next, we extend an approach based on time-memory trade-offs whose goal is to improve Ohkubo, Suzuki, and Kinoshita's protocol. We show that in practice this approach reaches the same performances as Molnar and Wagner's method, without degrading privacy.
In this document we study the application of weighted proportional fairness to data flows in the Internet. We let the users set the weights of their connections in order to maximise the utility they get from the network. When combined with a pricing scheme where connections are billed by weight and time, such a system is known to maximise the total utility of the network. Our study case is a national Web cache server connected to long distance links. We propose two ways of weighting TCP connections by manipulating some parameters of the protocol and present results from simulations and prototypes. We finally discuss how proportional fairness could be used to implement an Internet with differentiated services.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.