Fingerprint Bit Research Articles

Towards Robust Fingerprinting of Relational Databases by Mitigating Correlation Attacks.

Database fingerprinting is widely adopted to prevent unauthorized data sharing and identify source of data leakages. Although existing schemes are robust against common attacks, their robustness degrades significantly if attackers utilize inherent correlations among database entries. In this paper, we demonstrate the vulnerability of existing schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and their integration. We provide robust fingerprinting against these attacks by developing mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes and preserve the utility of databases. We investigate the impact of correlation attacks and the performance of mitigation techniques using a real-world database. Our results show (i) high success rates of correlation attacks against existing fingerprinting schemes (e.g., integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of mitigation techniques (e.g., after mitigation, integrated correlation attack can only distort 3% fingerprint bits). Additionally, the mitigation techniques effectively alleviate correlation attacks even if (i) attackers have access to correlation models directly computed from the original database, while the database owner uses inaccurate correlation models, (ii) or attackers utilizes higher order of correlations than the database owner.

Robust fingerprinting of genomic databases.

MotivationDatabase fingerprinting has been widely used to discourage unauthorized redistribution of data by providing means to identify the source of data leakages. However, there is no fingerprinting scheme aiming at achieving liability guarantees when sharing genomic databases. Thus, we are motivated to fill in this gap by devising a vanilla fingerprinting scheme specifically for genomic databases. Moreover, since malicious genomic database recipients may compromise the embedded fingerprint (distort the steganographic marks, i.e. the embedded fingerprint bit-string) by launching effective correlation attacks, which leverage the intrinsic correlations among genomic data (e.g. Mendel’s law and linkage disequilibrium), we also augment the vanilla scheme by developing mitigation techniques to achieve robust fingerprinting of genomic databases against correlation attacks.ResultsVia experiments using a real-world genomic database, we first show that correlation attacks against fingerprinting schemes for genomic databases are very powerful. In particular, the correlation attacks can distort more than half of the fingerprint bits by causing a small utility loss (e.g. database accuracy and consistency of SNP–phenotype associations measured via P-values). Next, we experimentally show that the correlation attacks can be effectively mitigated by our proposed mitigation techniques. We validate that the attacker can hardly compromise a large portion of the fingerprint bits even if it pays a higher cost in terms of degradation of the database utility. For example, with around 24% loss in accuracy and 20% loss in the consistency of SNP–phenotype associations, the attacker can only distort about 30% fingerprint bits, which is insufficient for it to avoid being accused. We also show that the proposed mitigation techniques also preserve the utility of the shared genomic databases, e.g. the mitigation techniques only lead to around 3% loss in accuracy.Availability and implementation https://github.com/xiutianxi/robust-genomic-fp-github.

The Curse of Correlations for Robust Fingerprinting of Relational Databases.

Database fingerprinting have been widely adopted to prevent unauthorized sharing of data and identify the source of data leakages. Although existing schemes are robust against common attacks, like random bit flipping and subset attack, their robustness degrades significantly if attackers utilize the inherent correlations among database entries. In this paper, we first demonstrate the vulnerability of existing database fingerprinting schemes by identifying different correlation attacks: column-wise correlation attack, row-wise correlation attack, and the integration of them. To provide robust fingerprinting against the identified correlation attacks, we then develop mitigation techniques, which can work as post-processing steps for any off-the-shelf database fingerprinting schemes. The proposed mitigation techniques also preserve the utility of the fingerprinted database considering different utility metrics. We empirically investigate the impact of the identified correlation attacks and the performance of mitigation techniques using real-world relational databases. Our results show (i) high success rates of the identified correlation attacks against existing fingerprinting schemes (e.g., the integrated correlation attack can distort 64.8% fingerprint bits by just modifying 14.2% entries in a fingerprinted database), and (ii) high robustness of the proposed mitigation techniques (e.g., with the mitigation techniques, the integrated correlation attack can only distort 3% fingerprint bits). Furthermore, we show that the proposed mitigation techniques effectively alleviate correlation attacks even if the attacker has access to the correlation models that are directly calculated from the database.

Density Prediction Models for Energetic Compounds Merely Using Molecular Topology.

Newly developed high-throughput methods for property predictions make the process of materials design faster and more efficient. Density is an important physical property for energetic compounds to assess detonation velocity and detonation pressure, but the time cost of recent density prediction models is still high owing to the time-consuming processes to calculate molecular descriptors. To improve the screening efficiency of potential energetic compounds, new methods for density prediction with more accuracy and less time cost are urgently needed, and a possible solution is to establish direct mappings between the molecular structure and density. We propose three machine learning (ML) models, support vector machine (SVM), random forest (RF), and Graph neural network (GNN), using molecular topology as the only known input. The widely applied quantitative structure-property relationship based on the density functional theory (DFT-QSPR) is adopted as the benchmark to evaluate the accuracies of the models. All these four models are trained and tested by using the same data set enclosing over 2000 reported nitro compounds searched out from the Cambridge Structural Database. The proportions of compounds with prediction error less than 5% are evaluated by using the independent test set, and the values for the models of SVM, RF, DFT-QSPR, and GNN are 48, 63, 85, and 88%, respectively. The results show that, for the models of SVM and RF, fingerprint bit vectors alone are not facilitated to obtain good QSPRs. Mapping between the molecular structure and density can be well established by using GNN and molecular topology, and its accuracy is slightly better than that of the time-consuming DFT-QSPR method. The GNN-based model has higher accuracy and lower computational resource cost than the widely accepted DFT-QSPR model, so it is more suitable for high-throughput screening of energetic compounds.

Noise attenuation and ridge processing technique for fingerprint bit minimization

Noise attenuation and ridge processing technique for fingerprint bit minimization

Segmental DCT Coefficient Reversal Based Anti-Collusion Audio Fingerprinting Mechanism

Collusion attacks are challenging to tackle in audio fingerprinting. A new direction to resist collusion attacks is to degrade the perceptual quality of the colluded files so that these files cannot be reused. The existing method in this direction has low embedding capacity and limited anti-collusion performance when the number of colluders is odd. In this letter, we present an anti-collusion mechanism that has a higher embedding capacity and can significantly degrade the perceptual quality of the colluded files regardless of the number of colluders. In the proposed mechanism, we first segment the host audio file into frames and perform the discrete cosine transform (DCT) on each frame. Then multiple fingerprint bits are embedded into each frame by reversing the DCT coefficients of the corresponding frequency band. As a result, when a collusion attack occurs, our proposed embedding mechanism can introduce perceptibly annoying differences between frames in the colluded file, which leads to severe perceptual quality degradation. Theoretical analysis and experimental results validate the superiority of the proposed anti-collusion mechanism.

Ternary Fingerprints with Reference Odor for Fluctuation-Enhanced Sensing.

An improved method for fluctuation-enhanced sensing (FES) is introduced. We enhanced the old binary fingerprinting method, where the fingerprint bit values were ±1, by introducing ternary fingerprint bits utilizing a reference odor. In the ternary method, the fingerprint bit values are −1, 0, and +1, where the 0 value stands for the situation where the slope of the spectrum is identical to that of the reference odor. The application of the reference odor spectrum makes the fingerprint relative to the reference. The ternary nature and the reference feature increase the information entropy of the fingerprints. The method is briefly illustrated by sensing bacterial odor in cow manure isolates.

XOR-Folding for Bloom Filter-based Encryptions for Privacy-preserving Record Linkage

In chemo-informatics, XOR-folding of fingerprint bit vectors is a prominent technique for speeding up searches in databases. For Privacy-preserving Record Linkage (PPRL), folding techniques have been applied using Multibit Trees for rapid linking. In this paper, we propose using this technique on Bloom Filters as used in PPRL, to prevent bit pattern attacks. We test the method in terms of linkage quality and decoding rate as defined in [12,11] using real-word data from a German phone book. Our results show no reduction in linkage quality using single XOR folding. However, the folding prevents bit pattern attacks completely. Another advantageous property of the folded Bloom Filters is the reduced length, which leads to a 50% decrease in memory consumption and a reduction in the time required for blocking and linking the data. Therefore, we consider the technique demonstrated in this paper as a promising candidate to be examined in further systematic tests of the cryptographic properties of hardened Bloom Filters for PPRL applications.

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods.

S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer's disease. However, the sparsity of atomic level data, such as protein-protein interaction of S100A9 with RAGE, TLR4/MD2, or CD147 (EMMPRIN) hinders the rational drug design of S100A9 inhibitors. Herein we first report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability and high cost-effectiveness. Notably, optimal feature sets were obtained after the reduction of 2,798 features into dozens of features with the chopping of fingerprint bits. Moreover, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through a consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into designing novel drugs targeting S100A9.

FingerPrint Based Duplicate Detection in Streamed Data

In computing, duplicate data detection refers to identifying duplicate copies of repeating data. Identifying duplicate data items in streamed data and eliminating them before storing, is a complex job. This paper proposes a novel data structure for duplicate detection using a variant of stable Bloom filter named as FingerPrint Stable Bloom Filter (FP-SBF). The proposed approach uses counting Bloom filter with fingerprint bits along with an optimization mechanism for duplicate detection. FP-SBF uses d-left hashing which reduces the computational time and decreases the false positives as well as false negatives. FP-SBF can process unbounded data in single pass, using k hash functions, and successfully differentiate between duplicate and distinct elements in O(k+1) time, independent of the size of incoming data. The performance of FP-SBF has been compared with various Bloom Filters used for stream data duplication detection and it has been theoretically and experimentally proved that the proposed approach efficiently detects the duplicates in streaming data with less memory requirements.

Binding mode of conformations and structure-based pharmacophore development for farnesyltransferase inhibitors

Farnesyltransferase (FTase) is one of the prenyltransferase family enzymes that catalyse the transfer of 15-membered isoprenoid (farnesyl) moiety to the cysteine of CAAX motif-containing proteins including Rho and Ras family of G proteins. Inhibitors of FTase act as drugs for cancer, malaria, progeria and other diseases. In the present investigation, we have developed two structure-based pharmacophore models from protein–ligand complex (3E33 and 3E37) obtained from the protein data bank. Molecular dynamics (MD) simulations were performed on the complexes, and different conformers of the same complex were generated. These conformers were undergone protein–ligand interaction fingerprint (PLIF) analysis, and the fingerprint bits have been used for structure-based pharmacophore model development. The PLIF results showed that Lys164, Tyr166, TrpB106 and TyrB361 are the major interacting residues in both the complexes. The RMSD and RMSF analyses on the MD-simulated systems showed that the absence of FPP in the complex 3E37 has significant effect in the conformational changes of the ligands. During this conformational change, some interactions between the protein and the ligands are lost, but regained after some simulations (after 2 ns). The structure-based pharmacophore models showed that the hydrophobic and acceptor contours are predominantly present in the models. The pharmacophore models were validated using reference compounds, which significantly identified as HITs with smaller RMSD values. The developed structure-based pharmacophore models are significant, and the methodology used in this study is novel from the existing methods (the original X-ray crystallographic coordination of the ligands is used for the model building). In our study, along with the original coordination of the ligand, different conformers of the same complex (protein–ligand) are used. It concluded that the developed methodology is significant for the virtual screening of novel molecules on different targets.

Molecular dynamic simulations and structure-based pharmacophore development for farnesyltransferase inhibitors discovery

Farnesyltransferase is one of the enzyme targets for the development of drugs for diseases, including cancer, malaria, progeria, etc. In the present study, the structure-based pharmacophore models have been developed from five complex structures (1LD7, 1NI1, 2IEJ, 2ZIR and 2ZIS) obtained from the protein data bank. Initially, molecular dynamic (MD) simulations were performed for the complexes for 10 ns using AMBER 12 software. The conformers of the complexes (75) generated from the equilibrated protein were undergone protein–ligand interaction fingerprint (PLIF) analysis. The results showed that some important residues, such as LeuB96, TrpB102, TrpB106, ArgB202, TyrB300, AspB359 and TyrB361, are predominantly present in most of the complexes for interactions. These residues form side chain acceptor and surface (hydrophobic or π–π) kind of interactions with the ligands present in the complexes. The structure-based pharmacophore models were generated from the fingerprint bits obtained from PLIF analysis. The pharmacophore models have 3–4 pharmacophore contours consist of acceptor and metal ligation (Acc & ML), hydrophobic (HydA) and extended acceptor (Acc2) features with the radius ranging between 1–3 Å for Acc & ML and 1–2 Å for HydA. The excluded volumes of the pharmacophore contours radius are between 1–2 Å. Further, the distance between the interacting groups, root mean square deviation (RMSD), root mean square fluctuation (RMSF) and radial distribution function (RDF) analysis were performed for the MD-simulated proteins using PTRAJ module. The generated pharmacophore models were used to screen a set of natural compounds and database compounds to select significant HITs. We conclude that the developed pharmacophore model can be a significant model for the identification of HITs as FTase inhibitors.

Target enhanced 2D similarity search by using explicit biological activity annotations and profiles

BackgroundThe enriched biological activity information of compounds in large and freely-accessible chemical databases like the PubChem Bioassay Database has become a powerful research resource for the scientific research community. Currently, 2D fingerprint based conventional similarity search (CSS) is the most common widely used approach for database screening, but it does not typically incorporate the relative importance of fingerprint bits to biological activity.ResultsIn this study, a large-scale similarity search investigation has been carried out on 208 well-defined compound activity classes extracted from PubChem Bioassay Database. An analysis was performed to compare the search performance of three types of 2D similarity search approaches: 2D fingerprint based conventional similarity search approach (CSS), iterative similarity search approach with multiple active compounds as references (ISS), and fingerprint based iterative similarity search with classification (ISC), which can be regarded as the combination of iterative similarity search with active references and a reversed iterative similarity search with inactive references. Compared to the search results returned by CSS, ISS improves recall but not precision. Although ISC causes the false rejection of active hits, it improves the precision with statistical significance, and outperforms both ISS and CSS. In a second part of this study, we introduce the profile concept into the three types of searches. We find that the profile based non-iterative search can significantly improve the search performance by increasing the recall rate. We also find that profile based ISS (PBISS) and profile based ISC (PBISC) significantly decreases ISS search time without sacrificing search performance.ConclusionsOn the basis of our large-scale investigation directed against a wide spectrum of pharmaceutical targets, we conclude that ISC and ISS searches perform better than 2D fingerprint similarity searching and that profile based versions of these algorithms do nearly as well in less time. We also suggest that the profile version of the iterative similarity searches are both better performing and potentially quicker than the standard algorithm.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0103-5) contains supplementary material, which is available to authorized users.

An Asymmetric Matching Method for a Robust Binary Audio Fingerprinting

Audio fingerprinting has been successfully applied for the problems associated with the protection of intellectual property, management of large database and indexation of content. For a reliable fingerprinting system, improving fingerprint matching accuracy is crucial. In this letter, we try to improve a binary audio fingerprint matching performance by utilizing auxiliary information which is discarded while extracting fingerprint bits. Since it is not feasible to store all the auxiliary information in the fingerprint DB, we only utilize the auxiliary information obtained while extracting fingerprints from the input unknown audio. We call the proposed method an asymmetric fingerprint matching, since the auxiliary information is available only in one side. Experimental results show that the proposed asymmetric matching method is effective in improving fingerprint matching performance over the conventional Hamming distance.

Near-duplicate document detection with improved similarity measurement

To quickly find documents with high similarity in existing documentation sets, fingerprint group merging retrieval algorithm is proposed to address both sides of the problem: a given similarity threshold could not be too low and fewer fingerprints could lead to low accuracy. It can be proved that the efficiency of similarity retrieval is improved by fingerprint group merging retrieval algorithm with lower similarity threshold. Experiments with the lower similarity threshold r=0.7 and high fingerprint bits k=400 demonstrate that the CPU time-consuming cost decreases from 1 921 s to 273 s. Theoretical analysis and experimental results verify the effectiveness of this method.

Modeling and Analysis of Correlated Binary Fingerprints for Content Identification

Multimedia identification via content fingerprints is used in many applications, such as content filtering on user-generated content websites, and automatic multimedia identification and tagging. A compact “fingerprint” is computed for each multimedia signal that captures robust and unique properties of the perceptual content, which is later used for identifying the multimedia. Several different multimedia fingerprinting schemes have been proposed in the literature and have been evaluated through experiments. To complement these experimental evaluations and provide guidelines for choosing system parameters and designing better schemes, this paper develops models for content fingerprinting and provides an analysis of the identification performance under these models. As a first step, bounds on the identification accuracy and the required fingerprint length for the simplest case when the fingerprint bits are modeled as i.i.d. are summarized. Markov Random Fields are then used to address more realistic settings of fingerprints with correlated components. The optimal likelihood ratio detector is derived and a statistical physics inspired approach for computing the probability of detection and probability of false alarm is described. The analysis shows that the commonly used Hamming distance detection criterion is susceptible to correlations among fingerprint bits, whereas the optimal log-likelihood ratio decision rule yields 5-20% improvement in the accuracy over a range of correlations. Simulation results demonstrate the validity of the theoretical predictions.

Rendering Conventional Molecular Fingerprints for Virtual Screening Independent of Molecular Complexity and Size Effects

Molecular complexity and size effects represent a known complication of fingerprint similarity searching and virtual screening that often leads to an increase in false-positive rates and a decrease in hit rates. In standard fingerprints, differences in the complexity of reference and database molecules lead to different fingerprint bit densities, which negatively affects similarity search calculations, in particular, when fingerprints of reference molecules have higher bit density than corresponding fingerprints of database compounds. In pharmaceutical research, this is the case in many practical virtual screening applications when chemically optimized reference molecules are used. Herein we introduce an intuitive computational method to make standard fingerprints such as structural keys or pharmacophore feature fingerprints independent of molecular complexity and size effects. This is achieved by applying the concept of 'balanced codes' originating in computer science. Following this approach, binary fingerprints are transformed by incorporating the complement of their bit patterns. This straightforward transformation produces fingerprint representations with characteristic bit patterns that have exactly half of their bit positions set on, corresponding to a constant bit density of 50 % for all test compounds, regardless of their complexity and size. In similarity search calculations in the presence of complexity effects of increasing magnitude, transformed structural key and pharmacophore fingerprints display consistently better performance than their unmodified counterparts and recover active compounds in cases where the original fingerprints fail.

Three‐Dimensional Protein–Ligand Interaction Scaling of Two‐Dimensional Fingerprints

We introduce a computational scaling methodology that utilizes protein-ligand interaction information extracted from complex crystal structures to enrich similarity searching using structural fingerprints with compound class-specific information. Scaling factors are derived to emphasize fingerprint bit positions that result from interacting fragments of bound ligands and correspond to frequently occurring structural features. Through interaction-based scaling, this information is transferred to standard fingerprints of multiple reference compounds. In systematic search calculations, fingerprints scaled on the basis of three-dimensional information are found to produce higher recall rates of active compounds than alternative types of scaled and non-scaled fingerprints.

Shannon Entropy-Based Fingerprint Similarity Search Strategy

For fingerprint searching using multiple active reference compounds, an information entropy-based similarity method is introduced as an alternative to conventional similarity coefficients and search strategies. The approach involves the determination of the fingerprint bit pattern entropy of a compound reference set and recalculation of the entropy following the addition of individual test compounds. If a database compound shares similar bit patterns with reference set molecules, adding this compound to the reference set only produces a small change in system entropy. By contrast, inclusion of a compound having a dissimilar fingerprint leads to a notable increase in entropy. Thus, database compounds can be screened for candidate molecules that do not cause significant changes in reference set fingerprint entropy. Compared to nearest neighbor methods, this approach has the computational advantage that it extracts reference set information only once prior to similarity searching. Test calculations on different compound data sets, fingerprints, and screening databases reveal that the ability of our entropy-based method to detect active compounds is often superior to data fusion techniques and Tanimoto similarity calculations.

Development of a Fingerprint Reduction Approach for Bayesian Similarity Searching Based on Kullback−Leibler Divergence Analysis

The contribution of individual fingerprint bit positions to similarity search performance is systematically evaluated. A method is introduced to determine bit significance on the basis of Kullback-Leibler divergence analysis of bit distributions in active and database compounds. Bit divergence analysis and Bayesian compound screening share a common methodological foundation. Hence, given the significance ranking of all individual bit positions comprising a fingerprint, subsets of bits are evaluated in the context of Bayesian screening, and minimal fingerprint representations are determined that meet or exceed the search performance of unmodified fingerprints. For fingerprints of different design evaluated on many compound activity classes, we consistently find that subsets of fingerprint bit positions are responsible for search performance. In part, these subsets are very small and contain in some cases only a few fingerprint bit positions. Structural or pharmacophore patterns captured by preferred bit positions can often be directly associated with characteristic features of active compounds. In some cases, reduced fingerprint representations clearly exceed the search performance of the original fingerprints. Thus, fingerprint reduction likely represents a promising approach for practical applications.