Using Bloom Filters to Ensure Access Control and Authentication Requirements for Scada Field Devices
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Implementing Signatures for Transactional Memory
Appears in the Proceedings of the 40th Annual IEEE/ACM Symposium on Microarchitecture (MICRO-40), 2007 Implementing Signatures for Transactional Memory Daniel Sanchez, Luke Yen, Mark D. Hill, Karthikeyan Sankaralingam Department of Computer Sciences, University of Wisconsin-Madison http://www.cs.wisc.edu/multifacet/logtm {daniel, lyen, markhill, karu}@cs.wisc.edu Abstract An important aspect of conflict detection is recording the addresses that a transaction reads (read set) and writes Transactional Memory (TM) systems must track the (write set) at some granularity (e.g., memory block or read and write sets—items read and written during a word). One promising approach is to use signatures, data transaction—to detect conflicts among concurrent trans- structures that can represent an unbounded number of el- actions. Several TMs use signatures, which summarize ements approximately in a bounded amount of state. Led unbounded read/write sets in bounded hardware at a per- by Bulk [7], several systems including LogTM-SE [31], formance cost of false positives (conflicts detected when BulkSC [8], and SigTM [17], have implemented read/write none exists). sets with per-thread hardware signatures built with Bloom This paper examines different organizations to achieve filters [2]. These systems track the addresses read/written hardware-efficient and accurate TM signatures. First, we in a transaction by inserting them into the read/write sig- find that implementing each signature with a single k-hash- natures, and clear both signatures as the transaction com- function Bloom filter (True Bloom signature) is inefficient, mits or aborts. Depending on the system, signatures must as it requires multi-ported SRAMs. -
Fast Candidate Generation for Real-Time Tweet Search with Bloom Filter Chains
Fast Candidate Generation for Real-Time Tweet Search with Bloom Filter Chains NIMA ASADI and JIMMY LIN, University of Maryland at College Park The rise of social media and other forms of user-generated content have created the demand for real-time search: against a high-velocity stream of incoming documents, users desire a list of relevant results at the time the query is issued. In the context of real-time search on tweets, this work explores candidate generation in a two-stage retrieval architecture where an initial list of results is processed by a second-stage rescorer 13 to produce the final output. We introduce Bloom filter chains, a novel extension of Bloom filters that can dynamically expand to efficiently represent an arbitrarily long and growing list of monotonically-increasing integers with a constant false positive rate. Using a collection of Bloom filter chains, a novel approximate candidate generation algorithm called BWAND is able to perform both conjunctive and disjunctive retrieval. Experiments show that our algorithm is many times faster than competitive baselines and that this increased performance does not require sacrificing end-to-end effectiveness. Our results empirically characterize the trade-off space defined by output quality, query evaluation speed, and memory footprint for this particular search architecture. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms: Algorithms, Experimentation Additional Key Words and Phrases: Scalability, efficiency, top-k retrieval, tweet search, bloom filters ACM Reference Format: Asadi, N. and Lin, J. 2013. Fast candidate generation for real-time tweet search with bloom filter chains. -
TS 102 176-1 V2.1.1 (2011-07) Technical Specification
ETSI TS 102 176-1 V2.1.1 (2011-07) Technical Specification Electronic Signatures and Infrastructures (ESI); Algorithms and Parameters for Secure Electronic Signatures; Part 1: Hash functions and asymmetric algorithms 2 ETSI TS 102 176-1 V2.1.1 (2011-07) Reference RTS/ESI-000080-1 Keywords e-commerce, electronic signature, security ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice Individual copies of the present document can be downloaded from: http://www.etsi.org The present document may be made available in more than one electronic version or in print. In any case of existing or perceived difference in contents between such versions, the reference version is the Portable Document Format (PDF). In case of dispute, the reference shall be the printing on ETSI printers of the PDF version kept on a specific network drive within ETSI Secretariat. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at http://portal.etsi.org/tb/status/status.asp If you find errors in the present document, please send your comment to one of the following services: http://portal.etsi.org/chaircor/ETSI_support.asp Copyright Notification No part may be reproduced except as authorized by written permission. -
An Optimal Bloom Filter Replacement∗
An Optimal Bloom Filter Replacement∗ Anna Pagh† Rasmus Pagh† S. Srinivasa Rao‡ Abstract store the approximation is roughly n log(1/)1, where This paper considers space-efficient data structures for n = |S| and the logarithm has base 2 [5]. In contrast, 0 0 the amount of space for storing S ⊆ {0, 1}w explicitly storing an approximation S to a set S such that S ⊆ S w 0 2 and any element not in S belongs to S with probability is at least log n ≥ n(w − log n) bits, which may be at most . The Bloom filter data structure, solving this much larger if w is large. Here, we consider subsets problem, has found widespread use. Our main result is of the set of w-bit machine words on a standard RAM a new RAM data structure that improves Bloom filters model, since this is the usual framework in which RAM in several ways: dictionaries are studied. Let the (random) set S0 ⊇ S consist of the elements • The time for looking up an element in S0 is O(1), that are stored (including false positives). We want S0 independent of . to be chosen such that any element not in S belongs to S0 with probability at most . For ease of exposition, we • The space usage is within a lower order term of the assume that ≤ 1/2 is an integer power of 2. A Bloom lower bound. filter [1] is an elegant data structure for selecting and representing a suitable set S0. It works by storing, as a • The data structure uses explicit hash function bit vector, the set families. -
Comparison on Search Failure Between Hash Tables and a Functional Bloom Filter
applied sciences Article Comparison on Search Failure between Hash Tables and a Functional Bloom Filter Hayoung Byun and Hyesook Lim * Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul 03760, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-3277-3403 Received:17 June 2020; Accepted: 27 July 2020; Published: 29 July 2020 Abstract: Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and d-left hash table. -
Invertible Bloom Lookup Tables
Invertible Bloom Lookup Tables Michael T. Goodrich Dept. of Computer Science University of California, Irvine http://www.ics.uci.edu/˜goodrich/ Michael Mitzenmacher Dept. of Computer Science Harvard University http://www.eecs.harvard.edu/˜michaelm/ Abstract We present a version of the Bloom filter data structure that supports not only the insertion, deletion, and lookup of key-value pairs, but also allows a complete listing of the pairs it contains with high probability, as long the number of key- value pairs is below a designed threshold. Our structure allows the number of key- value pairs to greatly exceed this threshold during normal operation. Exceeding the threshold simply temporarily prevents content listing and reduces the probability of a successful lookup. If entries are later deleted to return the structure below the threshold, everything again functions appropriately. We also show that simple variations of our structure are robust to certain standard errors, such as the deletion of a key without a corresponding insertion or the insertion of two distinct values for a key. The properties of our structure make it suitable for several applications, including database and networking applications that we highlight. 1 Introduction The Bloom filter data structure [5] is a well-known way of probabilistically supporting arXiv:1101.2245v3 [cs.DS] 4 Oct 2015 dynamic set membership queries that has been used in a multitude of applications (e.g., see [8]). The key feature of a standard Bloom filter is the way it trades off query accuracy for space efficiency, by using a binary array T (initially all zeroes) and k random hash functions, h1; : : : ; hk, to represent a set S by assigning T [hi(x)] = 1 for each x 2 S. -
The Hitchhiker's Guide to the SHA-3 Competition
History First Second Third The Hitchhiker’s Guide to the SHA-3 Competition Orr Dunkelman Computer Science Department 20 June, 2012 Orr Dunkelman The Hitchhiker’s Guide to the SHA-3 Competition 1/ 33 History First Second Third Outline 1 History of Hash Functions A(n Extremely) Short History of Hash Functions The Sad News about the MD/SHA Family 2 The First Phase of the SHA-3 Competition Timeline The SHA-3 First Round Candidates 3 The Second Round The Second Round Candidates The Second Round Process 4 The Third Round The Finalists Current Performance Estimates The Outcome of SHA-3 Orr Dunkelman The Hitchhiker’s Guide to the SHA-3 Competition 2/ 33 History First Second Third History Sad Outline 1 History of Hash Functions A(n Extremely) Short History of Hash Functions The Sad News about the MD/SHA Family 2 The First Phase of the SHA-3 Competition Timeline The SHA-3 First Round Candidates 3 The Second Round The Second Round Candidates The Second Round Process 4 The Third Round The Finalists Current Performance Estimates The Outcome of SHA-3 Orr Dunkelman The Hitchhiker’s Guide to the SHA-3 Competition 3/ 33 History First Second Third History Sad A(n Extremely) Short History of Hash Functions 1976 Diffie and Hellman suggest to use hash functions to make digital signatures shorter. 1979 Salted passwords for UNIX (Morris and Thompson). 1983/4 Davies/Meyer introduce Davies-Meyer. 1986 Fiat and Shamir use random oracles. 1989 Merkle and Damg˚ard present the Merkle-Damg˚ard hash function. -
Scalable Cooperative Caching Algorithm Based on Bloom Filters Nodirjon Siddikov
Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 2011 Scalable cooperative caching algorithm based on bloom filters Nodirjon Siddikov Follow this and additional works at: http://scholarworks.rit.edu/theses Recommended Citation Siddikov, Nodirjon, "Scalable cooperative caching algorithm based on bloom filters" (2011). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by the Thesis/Dissertation Collections at RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Scalable Cooperative Caching Algorithm based on Bloom Filters Nodirjon Siddikov Email: [email protected] M.S. in Computer Science Department of Computer Science B. Thomas Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, New York August, 2011 A Thesis submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science COMMITEE MEMBERS: _____________________________________________________________________________ CHAIR: Hans-Peter Bischof DATE Professor, Department of Computer Science _____________________________________________________________________________ READER: Minseok Kwon DATE Associate Professor, Department of Computer Science _____________________________________________________________________________ OBSERVER: James Heliotis DATE Professor, Department of -
Cryptography Knowledge Area (Draft for Comment)
CRYPTOGRAPHY KNOWLEDGE AREA (DRAFT FOR COMMENT) AUTHOR: Nigel Smart – KU Leuven EDITOR: George Danezis – University College London REVIEWERS: Dan Bogdanov - Cybernetica Kenny Patterson – Royal Holloway, University of London Liqun Chen – University of Surrey Cryptography Nigel P. Smart April 2018 INTRODUCTION The purpose of this chapter is to explain the various aspects of cryptography which we feel should be known to an expert in cyber-security. The presentation is at a level needed for an instructor in a module in cryptography; so they can select the depth needed in each topic. Whilst not all experts in cyber-security need be aware of all the technical aspects mentioned below, we feel they should be aware of all the overall topics and have an intuitive grasp as to what they mean, and what services they can provide. Our focus is mainly on primitives, schemes and protocols which are widely used, or which are suitably well studied that they could be used (or are currently being used) in specific application domains. Cryptography by its very nature is one of the more mathematical aspects of cyber-security; thus this chapter contains a lot more mathematics than one has in some of the other chapters. The overall presentation assumes a basic knowledge of either first-year undergraduate mathematics, or that found in a discrete mathematics course of an undergraduate Computer Science degree. The chapter is structured as follows: After a quick recap on some basic mathematical notation (Sec- tion 1), we then give an introduction to how security is defined in modern cryptography. This section (Section 2) forms the basis of our discussions in the other sections. -
NISTIR 7620 Status Report on the First Round of the SHA-3
NISTIR 7620 Status Report on the First Round of the SHA-3 Cryptographic Hash Algorithm Competition Andrew Regenscheid Ray Perlner Shu-jen Chang John Kelsey Mridul Nandi Souradyuti Paul NISTIR 7620 Status Report on the First Round of the SHA-3 Cryptographic Hash Algorithm Competition Andrew Regenscheid Ray Perlner Shu-jen Chang John Kelsey Mridul Nandi Souradyuti Paul Information Technology Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899-8930 September 2009 U.S. Department of Commerce Gary Locke, Secretary National Institute of Standards and Technology Patrick D. Gallagher, Deputy Director NISTIR 7620: Status Report on the First Round of the SHA-3 Cryptographic Hash Algorithm Competition Abstract The National Institute of Standards and Technology is in the process of selecting a new cryptographic hash algorithm through a public competition. The new hash algorithm will be referred to as “SHA-3” and will complement the SHA-2 hash algorithms currently specified in FIPS 180-3, Secure Hash Standard. In October, 2008, 64 candidate algorithms were submitted to NIST for consideration. Among these, 51 met the minimum acceptance criteria and were accepted as First-Round Candidates on Dec. 10, 2008, marking the beginning of the First Round of the SHA-3 cryptographic hash algorithm competition. This report describes the evaluation criteria and selection process, based on public feedback and internal review of the first-round candidates, and summarizes the 14 candidate algorithms announced on July 24, 2009 for moving forward to the second round of the competition. The 14 Second-Round Candidates are BLAKE, BLUE MIDNIGHT WISH, CubeHash, ECHO, Fugue, Grøstl, Hamsi, JH, Keccak, Luffa, Shabal, SHAvite-3, SIMD, and Skein. -
Bloom Filter-2
BALLS, BINS, AND RANDOM GRAPHS We use the Chernoff bound for the Poisson distribution (Theorem 5.4) to bound this probability, writing the bound as Pr(X 2: x) :s eX-ill-X In(x/m). For x = m + J2m In m, we use that In(l + z) 2: z - z2/2 for z 2: 0 to show Pr( X > m + J 2m In m ) :s e J2m In 111- (m+J2mlnm ) In(I+J21nm/m ) :s eJ2mTtlm -(III+J2mlnm )(J21nm/m-lnm/m) = e-lnlll+J2mlnlll(lnm/m) = 0(1). A similar argument holds if x < m, so Pr(IX - ml > J2m Inm) = 0(1). We now show the second fact, that IPr(E I IX - ml :s J2m Inm) - Pr(E I X = m)1 = 0(1). Note that Pr(E I X = k) is increasing in k, since this probability corresponds to the probability that all bins are nonempty when k balls are thrown independently and uni formly at random. The more balls that are thrown, the more likely all bins are nonempty. It follows that Pr(E I X = m - J2mlnm):s Pr(E IIX -ml:S J2mlnm) :s Pr(E I X = m + J2m Inm). Hence we have the bound IPr(E I IX - ml :s J2m Inm) - Pr(E I X = m)1 :s Pr(E I X = m + J2m Inm) - Pr(E I X = m - J2m Inm), and we show the right-hand side is 0(1). This is the difference between the probability that all bins receive at least one ball when 111 - J 2111 In 111 balls are thrown and when m + J2m In m balls are thrown. -
Year 2010 Issues on Cryptographic Algorithms
Year 2010 Issues on Cryptographic Algorithms Masashi Une and Masayuki Kanda In the financial sector, cryptographic algorithms are used as fundamental techniques for assuring confidentiality and integrity of data used in financial transactions and for authenticating entities involved in the transactions. Currently, the most widely used algorithms appear to be two-key triple DES and RC4 for symmetric ciphers, RSA with a 1024-bit key for an asymmetric cipher and a digital signature, and SHA-1 for a hash function according to international standards and guidelines related to the financial transactions. However, according to academic papers and reports regarding the security evaluation for such algorithms, it is difficult to ensure enough security by using the algorithms for a long time period, such as 10 or 15 years, due to advances in cryptanalysis techniques, improvement of computing power, and so on. To enhance the transition to more secure ones, National Institute of Standards and Technology (NIST) of the United States describes in various guidelines that NIST will no longer approve two-key triple DES, RSA with a 1024-bit key, and SHA-1 as the algorithms suitable for IT systems of the U.S. Federal Government after 2010. It is an important issue how to advance the transition of the algorithms in the financial sector. This paper refers to issues regarding the transition as Year 2010 issues in cryptographic algorithms. To successfully complete the transition by 2010, the deadline set by NIST, it is necessary for financial institutions to begin discussing the issues at the earliest possible date. This paper summarizes security evaluation results of the current algorithms, and describes Year 2010 issues, their impact on the financial industry, and the transition plan announced by NIST.