Combination Of Hashing Research Articles

CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.

Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model's performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).

FPGA-Based High-Throughput Key-Value Store Using Hashing and B-Tree for Securities Trading System

Field-Programmable Array (FPGA) technology is extensively used in Finance. This paper describes a high-throughput key-value store (KVS) for securities trading system applications using an FPGA. The design uses a combination of hashing and B-Tree techniques and supports a large number of keys (40 million) as required by the Trading System. We have used a novel technique of using buckets of different capacities to reduce the amount of Block-RAM (BRAM) and perform a high-speed lookup. The design uses high-bandwidth-memory (HBM), an On-chip memory available in Virtex Ultrascale+ FPGAs to support a large number of keys. Another feature of this design is the replication of the database and lookup logic to increase the overall throughput. By implementing multiple lookup engines in parallel and replicating the database, we could achieve high throughput (up to 6.32 million search operations/second) as specified by our client, which is a major stock exchange. The design has been implemented with a combination of Verilog and high-level-synthesis (HLS) flow to reduce the implementation time.

Recombinant Sort: N-Dimensional Cartesian Spaced Algorithm Designed from Synergetic Combination of Hashing, Bucket, Counting and Radix Sort

Sorting is an essential operation which is widely used and is fundamental to some very basic day to day utilities like searches, databases, social networks and much more. Optimizing this basic operation in terms of complexity as well as efficiency is cardinal. Optimization is achieved with respect to space and time complexities of the algorithm. In this paper, a novel left-field N-dimensional cartesian spaced sorting method is proposed by combining the best characteristics of bucket sort, counting sort and radix sort, in addition to employing hashing and dynamic programming for making the method more efficient. Comparison between the proposed sorting method and various existing sorting methods like bubble sort, insertion sort, selection sort, merge sort, heap sort, counting sort, bucket sort, etc., has also been performed. The time complexity of the proposed model is estimated to be linear i.e.

Generic Attacks on Hash Combiners

Hash combiners are a practical way to make cryptographic hash functions more tolerant to future attacks and compatible with existing infrastructure. A combiner combines two or more hash functions in a way that is hopefully more secure than each of the underlying hash functions, or at least remains secure as long as one of them is secure. Two classical hash combiners are the exclusive-or (XOR) combiner \( \mathcal {H}_1(M) \oplus \mathcal {H}_2(M) \) and the concatenation combiner \( \mathcal {H}_1(M) \Vert \mathcal {H}_2(M) \). Both of them process the same message using the two underlying hash functions in parallel. Apart from parallel combiners, there are also cascade constructions sequentially calling the underlying hash functions to process the message repeatedly, such as Hash-Twice \(\mathcal {H}_2(\mathcal {H}_1(IV, M), M)\) and the Zipper hash \(\mathcal {H}_2(\mathcal {H}_1(IV, M), \overleftarrow{M})\), where \(\overleftarrow{M}\) is the reverse of the message M. In this work, we study the security of these hash combiners by devising the best-known generic attacks. The results show that the security of most of the combiners is not as high as commonly believed. We summarize our attacks and their computational complexities (ignoring the polynomial factors) as follows:1.Several generic preimage attacks on the XOR combiner:A first attack with a best-case complexity of \( 2^{5n/6} \) obtained for messages of length \( 2^{n/3} \). It relies on a novel technical tool named interchange structure. It is applicable for combiners whose underlying hash functions follow the Merkle–Damgård construction or the HAIFA framework.A second attack with a best-case complexity of \( 2^{2n/3} \) obtained for messages of length \( 2^{n/2} \). It exploits properties of functional graphs of random mappings. It achieves a significant improvement over the first attack but is only applicable when the underlying hash functions use the Merkle–Damgård construction.An improvement upon the second attack with a best-case complexity of \( 2^{5n/8} \) obtained for messages of length \( 2^{5n/8} \). It further exploits properties of functional graphs of random mappings and uses longer messages. These attacks show a rather surprising result: regarding preimage resistance, the sum of two n-bit narrow-pipe hash functions following the considered constructions can never provide n-bit security.2.A generic second-preimage attack on the concatenation combiner of two Merkle–Damgård hash functions. This attack finds second preimages faster than \( 2^n \) for challenges longer than \( 2^{2n/7} \) and has a best-case complexity of \( 2^{3n/4} \) obtained for challenges of length \( 2^{3n/4} \). It also exploits properties of functional graphs of random mappings.3.The first generic second-preimage attack on the Zipper hash with underlying hash functions following the Merkle–Damgård construction. The best-case complexity is \( 2^{3n/5} \), obtained for challenge messages of length \( 2^{2n/5} \).4.An improved generic second-preimage attack on Hash-Twice with underlying hash functions following the Merkle–Damgård construction. The best-case complexity is \( 2^{13n/22} \), obtained for challenge messages of length \( 2^{13n/22} \).The last three attacks show that regarding second-preimage resistance, the concatenation and cascade of two n-bit narrow-pipe Merkle–Damgård hash functions do not provide much more security than that can be provided by a single n-bit hash function. Our main technical contributions include the following:1.The interchange structure, which enables simultaneously controlling the behaviours of two hash computations sharing the same input.2.The simultaneous expandable message, which is a set of messages of length covering a whole appropriate range and being multi-collision for both of the underlying hash functions.3.New ways to exploit the properties of functional graphs of random mappings generated by fixing the message block input to the underlying compression functions.

Functional Graphs and Their Applications in Generic Attacks on Iterated Hash Constructions

We provide a survey about generic attacks on cryptographic hash constructions including hash-based message authentication codes and hash combiners. We look into attacks involving iteratively evaluating identical mappings many times. The functional graph of a random mapping also involves iteratively evaluating the mapping. These attacks essentially exploit properties of the functional graph. We map the utilization space of those properties from numerous proposed known attacks, draw a comparison among classes of attacks about their advantages and limitations. We provide a systematic exposition of concepts of cycles, deep-iterate images, collisions and their roles in cryptanalysis of iterated hash constructions. We identify the inherent relationship between these concepts, such that case-by-case theories about them can be unified into one knowledge system, that is, theories on the functional graph of random mappings. We show that the properties of the cycle search algorithm, the chain evaluation algorithm and the collision search algorithm can be described based on statistic results on the functional graph. Thereby, we can provide different viewpoints to support previous beliefs on individual knowledge. In that, we invite more sophisticated analysis of the functional graph of random mappings and more future exploitations of its properties in cryptanalysis.

Functional Graphs and Their Applications in Generic Attacks on Iterated Hash Constructions

We provide a survey about generic attacks on cryptographic hash constructions including hash-based message authentication codes and hash combiners. We look into attacks involving iteratively evaluating identical mappings many times. The functional graph of a random mapping also involves iteratively evaluating the mapping. These attacks essentially exploit properties of the functional graph. We map the utilization space of those properties from numerous proposed known attacks, draw a comparison among classes of attacks about their advantages and limitations. We provide a systematic exposition of concepts of cycles, deep-iterate images, collisions and their roles in cryptanalysis of iterated hash constructions. We identify the inherent relationship between these concepts, such that case-by-case theories about them can be unified into one knowledge system, that is, theories on the functional graph of random mappings. We show that the properties of the cycle search algorithm, the chain evaluation algorithm and the collision search algorithm can be described based on statistic results on the functional graph. Thereby, we can provide different viewpoints to support previous beliefs on individual knowledge. In that, we invite more sophisticated analysis of the functional graph of random mappings and more future exploitations of its properties in cryptanalysis.

Virus Recognition Based on Combination of Hashing and Neural Networks

Virus Recognition Based on Combination of Hashing and Neural Networks

A Database De-identification Framework to Enable Direct Queries on Medical Data for Secondary Use

To qualify the use of patient clinical records as non-human-subject for research purpose, electronic medical record data must be de-identified so there is minimum risk to protected health information exposure. This study demonstrated a robust framework for structured data de-identification that can be applied to any relational data source that needs to be de-identified. Using a real world clinical data warehouse, a pilot implementation of limited subject areas were used to demonstrate and evaluate this new de-identification process. Query results and performances are compared between source and target system to validate data accuracy and usability. The combination of hashing, pseudonyms, and session dependent randomizer provides a rigorous de-identification framework to guard against 1) source identifier exposure; 2) internal data analyst manually linking to source identifiers; and 3) identifier cross-link among different researchers or multiple query sessions by the same researcher. In addition, a query rejection option is provided to refuse queries resulting in less than preset numbers of subjects and total records to prevent users from accidental subject identification due to low volume of data. This framework does not prevent subject re-identification based on prior knowledge and sequence of events. Also, it does not deal with medical free text de-identification, although text de-identification using natural language processing can be included due its modular design. We demonstrated a framework resulting in HIPAA Compliant databases that can be directly queried by researchers. This technique can be augmented to facilitate inter-institutional research data sharing through existing middleware such as caGrid.

Chaînage de bases de données anonymisées pour les études épidémiologiques multicentriques nationales et internationales : proposition d’un algorithme cryptographique

Background Compiling individual records which come from different sources remains very important for multicenter epidemiological studies, but at the same time European directives or other national legislations concerning nominal data processing have to be respected. These legal aspects can be satisfied by implementing mechanisms that allow anonymization of patient data (such as hashing techniques). Moreover, for security reasons, official recommendations suggest using different cryptographic keys in combination with a cryptographic hash function for each study. Unfortunately, such an anonymization procedure is in contradiction with the common requirement in public health and biomedical research as it becomes almost impossible to link records from separate data collections where the same entity is not referenced in the same way. Solving this paradox by using methodology based on the combination of hashing and enciphering techniques is the main aim of this article. Methods The method relies on one of the best known hashing functions (the secure hash algorithm) to ensure the anonymity of personal information while providing greater resistance to dictionary attacks, combined with encryption techniques. The originality of the method relies on the way the combination of hashing and enciphering techniques is performed: like in asymmetric encryption, two keys are used but the private key depends on the patient's identity. Results The combination of hashing and enciphering techniques provides a great improvement in the overall security of the proposed scheme. Conclusion This methodology makes the stored data available for use in the field of public health for the benefit of patients, while respecting legal security requirements.

Linking anonymous databases for national and international multicenter epidemiological studies: A cryptographic algorithm

Background Compiling individual records coming from different sources is very important for multicenter epidemiological studies; however, European directives and other national legislation concerning nominal data processing must be respected. These legal aspects can be satisfied by implementing mechanisms that allow anonymization of patient data (such as hashing techniques). Moreover, for security reasons, official recommendations suggest using different cryptographic keys in combination with a cryptographic hash function for each study. Unfortunately, this type of anonymization procedure is in contradiction with common requirements in public health and biomedical research because it becomes almost impossible to link records from separate data collections where the same entity is not referenced in the same way. Solving this paradox using a methodology based on the combination of hashing and enciphering techniques is the main aim of this article. Methods The method relies on one of the best-known hashing functions (the Secure Hash Algorithm) to ensure the anonymity of personal information while providing greater resistance to dictionary attacks, combined with encryption techniques. The originality of the method lies in how the hashing and enciphering techniques are combined: as in asymmetric encryption, two keys are used but the private key depends on the patient's identity. Results The combination of hashing and enciphering techniques greatly improves the overall security of the proposed scheme. Conclusion This methodology makes the stored data available for use in the field of public health for the benefit of patients, while respecting legal and security requirements.

Combining Hashing and Enciphering Algorithms for Epidemiological Analysis of Gathered Data

Compiling individual records coming from different sources is necessary for multi-center studies. Legal aspects can be satisfied by implementing anonymization procedures. When using these procedures with a different key for each study it becomes almost impossible to link records from separate data collections. The originality of the method relies on the way the combination of hashing and enciphering techniques is performed: like in asymmetric encryption, two keys are used but the private key depends on the patient's identity. The combination of hashing and enciphering techniques provides a great improvement in the overall security of the proposed scheme. This methodology makes stored data available for use in the field of public health, while respecting legal security requirements.

Bounded disorder file organization

The bounded disorder file organization proposed by W. Litwin and D.B. Lomet (1987) uses a combination of hashing and tree indexing. Lomet provided an approximate analysis with the mention of the difficulty involved in exact modeling of data nodes, which motivated this work. In an earlier paper (M.V. Ramakrishna and P. Mukhopadhyay, 1988) we provided an exact model and analysis of the data nodes, which is based on the solution of a classical sequential occupancy problem. After summarizing the analysis of data nodes, an alternate file growth method based on repeated trials using universal hashing is proposed and analyzed. We conclude that the alternate file growth method provides simplicity and significant improvement in storage utilization.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A predicate matching algorithm for database rule systems

Forward-chaining rule systems must test each newly asserted fact against a collection of predicates to find those rules that match the fact. Expert system rule engines use a simple combination of hashing and sequential search for this matching. We introduce an algorithm for finding the matching predicates that is more efficient than the standard algorithm when the number of predicates is large. We focus on equality and inequality predicates on totally ordered domains. This algorithm is well-suited for database rule systems, where predicate-testing speed is critical. A key component of the algorithm is the interval binary search tree (IBS-tree). The IBS-tree is designed to allow efficient retrieval of all intervals (e.g. range predicates) that overlap a point, while allowing dynamic insertion and deletion of intervals. The algorithm could also be used to improve the performance of forward-chaining inference engines for large expert systems applications.