Amount Of False Positives Research Articles

Multi-Classification of Fetal Health Status Using Extreme Learning Machine

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive. When multi-class classification was performed, the accuracy was obtained as 98.12%. The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms

Human enterprise often suffers from direct negative effects caused by jellyfish blooms. The investigation of a prior jellyfish monitoring system showed that it was unable to reliably perform in a cross validation setting, i.e. in new underwater environments. In this paper, a number of enhancements are proposed to the part of the system that is responsible for object classification. First, the training set is augmented by adding synthetic data, making the deep learning classifier able to generalise better. Then, the framework is enhanced by employing a new second stage model, which analyzes the outputs of the first network to make the final prediction. Finally, weighted loss and confidence threshold are added to balance out true and false positives. With all the upgrades in place, the system can correctly classify 30.16% (comparing to the initial 11.52%) of all spotted jellyfish, keep the amount of false positives as low as 0.91% (comparing to the initial 2.26%) and operate in real-time within the computational constraints of an autonomous embedded platform.

Validity of the patient health questionnaire-2 (PHQ-2) for the detection of depression in primary care in Colombia

BackgroundThe PHQ-2 is a screening test for major depressive disorder (MDD) derived from PHQ-9, which has shown to be useful in the detection of cases of clinical significance in previous studies. The psychometric properties of PHQ-2 in the Colombian population are unknown. MethodsPHQ-2 were assessed in 243 patients in general medical consultations using the Mini-International Neuropsychiatric Interview (MINI) as the gold standard. Internal consistency, convergent validity and criterion validity were calculated by analyzing the Receptor Operating Characteristics (ROC) and the area under the curve (AUC). ResultsCronbach's alpha and McDonald's omega coefficients were 0.71. Spearman's rho coefficients for correlations with PHQ-9 and HADS-D scores were 0.63 and 0.59 (p<0.01). AUC was 0.89. The optimal cut point was ≥2 with the following indicators: sensitivity 0.87 specificity 0.74; Youden index 0.60; PPV 0.47; NPV 0.95; (LR+) 3.24; (LR-) 0.18 (95% CI 0.09-0.37). Kappa coefficient between PHQ-2 and MINI for depression was .458 and 0.46 for HADS-D. LimitationsSince this study was done with people attending hospital, which could has implications for the prevalence of depression, affecting the validity indicators of the instrument. ConclusionsPHQ-2 show an acceptable test performance in the context of the study. However, the test could yield a considerable amount of false positives that would require specialized evaluation to establish a reliable diagnosis.

Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Semi-automatic measurements are performed on 18FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-automatic tools cannot make the difference between cancerous regions and active organs, presenting a high 18FDG uptake.In this work, we combine a deep learning-based approach with a superpixel segmentation method to segment the main active organs (brain, heart, bladder) from full-body PET images. In particular, we integrate a superpixel SLIC algorithm at different levels of a convolutional network. Results are compared with a deep learning segmentation network alone. The methods are cross-validated on full-body PET images of 36 patients and tested on the acquisitions of 24 patients from a different study center, in the context of the ongoing EPICUREseinmeta study. The similarity between the manually defined organ masks and the results is evaluated with the Dice score. Moreover, the amount of false positives is evaluated through the positive predictive value (PPV).According to the computed Dice scores, all approaches allow to accurately segment the target organs. However, the networks integrating superpixels are better suited to transfer knowledge across datasets acquired on multiple sites (domain adaptation) and are less likely to segment structures outside of the target organs, according to the PPV.Hence, combining deep learning with superpixels allows to segment organs presenting a high 18FDG uptake on PET images without selecting cancerous lesion, and thus improves the precision of the semi-automatic tools monitoring the evolution of breast cancer metastasis.Clinical relevance- We demonstrate the utility of combining deep learning and superpixel segmentation methods to accurately find the contours of active organs from metastatic breast cancer images, to different dataset distributions.

New software for automated cilia detection in cells (ACDC)

BackgroundPrimary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably—across different experimental data sets—without significantly compromising the accuracy of manual analysis.MethodsTo solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy ‘F1’ score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set.ResultsThe ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1–6%, yielding high F1 scores of 0.96–0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour.ConclusionThe ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.

Scene Text Detection with Supervised Pyramid Context Network

Scene text detection methods based on deep learning have achieved remarkable results over the past years. However, due to the high diversity and complexity of natural scenes, previous state-of-the-art text detection methods may still produce a considerable amount of false positives, when applied to images captured in real-world environments. To tackle this issue, mainly inspired by Mask R-CNN, we propose in this paper an effective model for scene text detection, which is based on Feature Pyramid Network (FPN) and instance segmentation. We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.Benefited from the guidance of semantic information and sharing FPN, SPCNET obtains significantly enhanced performance while introducing marginal extra computation. Experiments on standard datasets demonstrate that our SPCNET clearly outperforms start-of-the-art methods. Specifically, it achieves an F-measure of 92.1% on ICDAR2013, 87.2% on ICDAR2015, 74.1% on ICDAR2017 MLT and 82.9% on

A hierarchical hybrid framework for modelling anomalous behaviours

The presence of anomalies in collected information, i.e. data that deviates substantially from what is normally expected, is a valuable source of knowledge and its discovery has many practical applications. Anomaly-detection approaches rely on building models that suitably describe data patterns deemed as normal, however they may incur the generation of a considerable amount of false positives. Signature-based techniques, which exploit a prior knowledge base of anomalous patterns, are able to effectively detect them but fail in identifying anomalies which did not occur previously. Hybrid anomaly detection systems combine the two approaches in order to obtain better detection performances. This paper proposes a framework, called HALF, that allows to develop hybrid systems by combining available techniques, coming from both approaches. HALF is able to operate on any data type and provides native support to online learning, or concept drifting. This enables the incremental updating of the knowledge bases used by the techniques. HALF has been designed to accommodate multiple mining algorithms by organizing them in a hierarchical structure in order to offer an higher and flexible detection capability. The framework effectiveness is demonstrated through two case studies concerning a network intrusion detection system and a steganography hunting system.

Data-driven prediction of adverse drug reactions induced by drug-drug interactions

BackgroundThe expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects.MethodWe hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles.ResultsWe used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org. We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs via DDIs. This allowed us to identify potential DDI-induced ADRs not yet clinically reported. The ability of the models to quantify adverse effects between drug classes also suggests that we may be able to select drug combinations that minimize the risk of ADRs.ConclusionAlmost all information on DDI-induced ADRs is generated after drug approval. This situation poses significant health risks for vulnerable patient populations with comorbidities. To help mitigate the risks, we developed a robust probabilistic approach to prospectively predict DDI-induced ADRs. Based on this approach, we developed prediction models for 1,096 ADRs and used them to predict the propensity of all pairwise combinations of nearly 800 drugs to be associated with these ADRs via DDIs. We made the predictions publicly available via internet access.

Reliability of algorithmic somatic copy number alteration detection from targeted capture data.

MotivationWhole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.ResultsOn synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise (>90% correct calls) at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%).DiscussionWhole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods.Availability and implementationScripts are provided as supplementary information.Supplementary information Supplementary data are available at Bioinformatics online.

Стан мікробіоценозу статевої системи у жінок з гнійними запальними процесами органів малого таза

The aim of the study was to assess the state of microbiocenosis of the reproductive system in women with purulent inflammation of the pelvic organs. Patients and methods. The survey was conducted during 2012–2015 at the Municipal Clinical Hospital №1 in the city of Odessa (Ukraine). The study involved 27 women with verified purulent inflammation of the pelvic organs. The average age of patients was 41.3±1.2 years. Bacteriological analysis was performed in the clinical laboratory of the Municipal Clinical Hospital №1. The analysis was conducted using classic techniques with the elaboration of antibiotic-gram. The species composition of microorganisms isolated from abscesses, blood, purulent separation, drainage and postoperative wounds was obtained and studied in 27 patients during surgery and daily during the first week hospital stay. Results. It is shown that among septic diseases of the pelvic organs in patients with purulent dominated tubo-ovarian tumor (9 cases or 33.3%), pyosalpinx with perforations (33.3%). It is found that most often at the time of surgery wounds inoculated Staphylococcus epidermidis (66.7%), Staphylococcus saprophyticus (29.6%), as well as E. coli. (29.6%). Less common are inoculated bacteria like Streptococcus fecalis (14.8%), Enterobacter aeruginosa (3.7%) and Klebsiella pneumoniae (37%). In 40.7% of patients there were microbial associations of different composition, mainly represented by staphylococci and E. coli. It is shown that on the second day of the postoperative period bacteriological crops were negative, indicating the adequacy of the applied antibiotic treatment. Conclusion. Already on the second day postoperative bacteriological crops were negative, indicating the adequacy of the applied antibiotic. The results allow us to recommend the use of a basic combination therapy protected penicillins, ensuring the elimination of a wide spectrum of pathogens, including anaerobes, and doxycycline that the first two days of hospital stay administered parenterally. After lowering body temperature below 37.5°C and normalization leykohramy a transition to the use of oral antibiotics. We believe that the treatment of PID preference should be given antibiotic combination, while for the diagnosis of chlamydia should use highly specific methods that provide the least amount of false positives (PCR DNA combined with the sowing and the subsequent direct microscopy). What is important is a careful dose and duration of antibiotics and purpose of avoiding situations where patients with PID prescribed instead of causal treatment not justified pathogenetic therapy (immunomodulators, enzymes, adaptogens, etc.).The prospects of further studies related to the assessment of the dynamics of the content of acute-phase proteins in septic diseases of the pelvic organs. Key words: pelvic inflammation disease, microbiocenosis, diagnostics.

Fundus Image Screening for Diabetic Retinopathy

The proposed system aims in diagnosing Diabetic Retinopathy (DR), a snag in patients with Diabetes for prolonged periods. MinIMaS algorithm is primarily used for background Extraction and Gaussian Mixture Model (GMM) is used for grouping of lesions. The amount of false positives are minimized by the selection of feature vectors used for classification. The proposed system achieves sensitivity of 89% for grouping of bright lesion and sensitivity of 82% for classifying bright lesion which when compared to existing system (KNN classifier) gives a higher sensitivity value. Similarly MinIMaS algorithm provides 91% accurate results when compared to the existing Haar based wavelet transform (70%) and Highest Average Variation (65%). System aids in detecting severity of Diabetic Retinopthy with improved accuracy for timely treatment of patients.

Implications of Unprofitable Horizontal Mergers: A Positive External Effect Does Not Suffice To Clear A Merger!

We demonstrate that the popular Farrell-Shapiro-framework (FSF) for the analysis of mergers in oligopolies relies regarding its policy conclusions sensitively on the assumption that rational agents will only propose privately profitable mergers. If this assumption held, a positive external effect of a proposed merger would represent a sufficient condition to allow the merger. However, the empirical picture on mergers and acquisitions reveals a significant share of unprofitable mergers and economic theory, moreover, demonstrates that privately unprofitable mergers can be the result of rational action. Therefore, we extend the FSF by explicitly allowing for unprofitable mergers to occur with some frequency. This exerts a considerable impact on merger policy conclusions: while several insights of the original FSF are corroborated (f.i. efficiency defence), a positive external effect does not represent a sufficient condition for the allowance of a merger anymore. Applying such a rule would cause a considerable amount of false positives. We thank all participants of the 29th HOS Conference (Marburg, November 2007), the 35th EARIE Conference (Toulouse, September 2008) and the Annual Meeting of the Verein fur Socialpolitik (Graz, September 2008) for a valuable and helpful discussion of this paper. Furthermore, we thank Barbara Guldenring for editorial assistance. (This abstract was borrowed from another version of this item.)

Utilization of a Computerized Clinical Surveillance System to Increase Acute Myocardial Infarction Core Measure Compliance

Clinical surveillance systems (CSS) are utilized by many health care institutions to help identify at-risk patients, prioritize care, improve the clinical services provided, and increase compliance with the Center for Medicare and Medicaid Services (CMS) core measures. This study will assess the accuracy and practicality of a CMS acute myocardial infarction (AMI) alert, provided by the CSS Theradoc, designed to increase compliance with medications required at discharge in AMI patients. This is a single-center, retrospective cohort review to determine the sensitivity and specificity of the Theradoc CMS AMI alert. All patients with a Theradoc CMS AMI alert were analyzed for AMI occurrence and omission of CMS-required discharge medications. Secondary endpoints regarding alert times and quantity were also assessed to address the practicality of implementing the alert. Data were collected on patients with alerts occurring between January 1, 2011, and December 31, 2011 (N = 962). The Theradoc CSS alert was found to have a sensitivity of 100% and specificity of 4.79%, signifying a gross amount of false positives. No modifications to the alert definitions or timing were able to increase the specificity to >10%. Regardless of the high sensitivity, the Theradoc CMS AMI alert does not have the appropriate level of specificity to utilize at the study institution at this time.

The Pediatric Vision Screening Program Performed in Bolzano in 2010: Significance of the Orthoptic Re-Examination

Aim: To analyze results of the vision screening program performed in the Bolzano District and to evaluate the significance of orthoptic re-screening in children with unreliable results.Methods: In 2010, 7772 children, aged 7 months, 3 years, or 5 years, were invited for orthoptic vision screening. Children with unreliable screening findings were re-invited for a second test. In this retrospective study we examined the charts of all children who underwent screening and reported the outcome.Results: A total of 6126 children were examined; 1646 (21.2%) did not attend screening. Of the children tested, 562 (9.2%) showed positive screening results and were referred for ophthalmologic examination. There were 427 (7.0%) children with unreliable results who were re-examined after 3 to 6 months; among them, 307 (71.9%) showed a negative final result and 120 (28.1% of re-examined subgroup, 1.96% of the total) were positive.Conclusions: The large number of unreliable results at the first screening test could be caused by lack of collaboration. The orthoptic re-examination of children with unreliable results may be useful in reducing the amount of false positives.

Performance of two commercial rapid methods for sampling and detection of Listeria in small-scale cheese producing and salmon processing environments

Two commercially available all-in-one swab rapid detection systems for Listeria spp. (InSite Listeria Test and Path-Chek hygiene Listeria) were tested for performance in cheese production environments and salmon processing facilities. Sampling was conducted both on clean surfaces and during production. A total of 338 samples were taken using the swabs (175 in cheese environments, 163 in salmon environments). Conventional sampling using sterile cloths and standardized qualitative detection of Listeria spp. according to NMKL method no. 136 was performed in parallel from 64 sampling sites in the salmon processing facilities and 40 sampling sites in the cheese production facilities. Results showed that both rapid swab tests detected Listeria spp.; however, they returned significant amounts of false positives. Presence of Listeria spp. was indicated in 47% and 41% of all swabs in the cheese and salmon environments, respectively. Enrichment followed by selective plating and Listeria specific PCR confirmed none of the 82 presumptive positive swabs from the cheese environment and 16 of 67 presumptive positive swabs from the salmon environments, respectively. Further analysis showed that several other bacteria, including Enterococcus spp. and Carnobacterium maltaromaticum, were the source of false positive swab results. From salmon processing facilities, using cloth sampling and standard analyses, 22% Listeria positive sampling sites were confirmed compared to 9% and 11% positives obtained using InSite or Path-Chek detection systems. From the cheese production environments, no Listeria positive sites were confirmed using either swab or cloth sampling. In conclusion, the use of these rapid detection methods was not suited in the selected environments due to large numbers of false positives, caused by the background flora.

Similarity assessment for removal of noisy end user license agreements

In previous work, we have shown the possibility to automatically discriminate between legitimate software and spyware-associated software by performing supervised learning of end user license agreements (EULAs). However, the amount of false positives (spyware classified as legitimate software) was too large for practical use. In this study, the false positives problem is addressed by removing noisy EULAs, which are identified by performing similarity analysis of the previously studied EULAs. Two candidate similarity analysis methods for this purpose are experimentally compared: cosine similarity assessment in conjunction with latent semantic analysis (LSA) and normalized compression distance (NCD). The results show that the number of false positives can be reduced significantly by removing noise identified by either method. However, the experimental results also indicate subtle performance differences between LSA and NCD. To improve the performance even further and to decrease the large number of attributes, the categorical proportional difference (CPD) feature selection algorithm was applied. CPD managed to greatly reduce the number of attributes while at the same time increase classification performance on the original data set, as well as on the LSA- and NCD-based data sets.

FALL DETECTION OF TWO-LEGGED WALKING ROBOTS USING MULTI-WAY PRINCIPAL COMPONENTS ANALYSIS

A bipedal robot will fall if it encounters large disturbances. If this fall is detected in advance, an emergency action can be taken to minimize damage or even to prevent the fall. Recently we introduced a new method for the detection of upcoming falls, called the Multi-way Principal Component Analysis (MPCA) detection method, and we showed that it is successful in detecting the fall of the simplest walking model. The aim of this paper is to study the performance of the MPCA detection method on bipedal prototypes. We implemented the detection method on one of our prototypes called 'Meta' and conducted a parameter study for two parameters: sampling time and number of planes used for modeling the nominal behavior. The performance of the detection method was quantified with the average detection time and the amount of false positives. The results of this study show that the MPCA detection method is only able to detect the fall of the prototype after the last stance leg transition before the fall. The detection time is improved for an increase in number of planes or a decrease of the sampling time, but it will never detect a fall before the last heel strike. Thus, the method is useful for triggering bracing actions, but not for preventive foot placement.

PromoterSweep: a tool for identification of transcription factor binding sites

There are many tools available for the prediction of potential promoter regions and the transcription factor binding sites (TFBS) harboured by them. Unfortunately, these tools cannot really avoid the prediction of vast amounts of false positives, the greatest problem in promoter analysis. The combination of different methods and algorithms has shown an improvement in prediction accuracy for similar biological problems such as gene prediction. The web-tool presented here uses this approach to perform an exhaustive integrative analysis, identification and annotation of potential promoter regions. The combination of methods employed includes searches in different experimental promoter databases to identify promoter regions and their orthologs, use of TFBS databases and search tools, and a phylogenetic footprinting strategy, combining multiple alignment of genomic sequences together with motif discovery tools that were tested previously in order to get the best method combination. The pipeline is available for academic users at the HUSAR open server http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar/. It integrates all of this information and identifies among the huge number of TFBS predictions those, which are more likely to be potentially functional.

Implications of Unprofitable Horizontal Mergers: A Re-Interpretation of the Farrell-Shapiro-Framework

We demonstrate that the popular Farrell-Shapiro-Framework (FSF) for the analysis of mergers in oligopolies relies regarding its policy conclusions sensitively on the assumption that rational agents will only propose privately profitable mergers. If this assumption held, a positive external effect of a proposed merger would represent a sufficient condition to allow the merger. However, the empirical picture on mergers and acquisitions reveals a significant share of unprofitable mergers and economic theory, moreover, demonstrates that privately unprofitable mergers can be the result of rational action. Therefore, we extend the FSF by explicitly allowing for unprofitable mergers to occur with some frequency. This exerts a considerable impact on merger policy conclusions: while several insights of the original FSF are corroborated (f.i. efficiency defence), a positive external effect does not represent a sufficient condition for the allowance of a merger anymore. Applying such a rule would cause a considerable amount of false positives. In addition, we conclude that the FSF need to be explicitly complemented by a freedom of competition principle in order to make it workable as a basis for an economics-based merger policy.

Succinct representation of flexible and privacy-preserving access rights

We explore the problem of portable and flexible privacy preserving access rights that permit access to a large collection of digital goods. Privacy-preserving access control means that the service provider can neither learn what access rights a customer has nor link a request to access an item to a particular customer, thus maintaining privacy of both customer activity and customer access rights. Flexible access rights allow a customer to choose a subset of items or groups of items from the repository, obtain access to and be charged only for the items selected. And portability of access rights means that the rights themselves can be stored on small devices of limited storage space and computational capabilities such as smartcards or sensors, and therefore the rights must be enforced using the limited resources available. In this paper, we present and compare two schemes that address the problem of such access rights. We show that much can be achieved if one allows for even a negligible amount of false positives – items that were not requested by the customer, but inadvertently were included in the customer access right representation due to constrained space resources. But minimizing false positives is one of many other desiderata that include protection against sharing of false positives information by unscrupulous users, providing the users with transaction untraceability and unlinkability, and forward compatibility of the scheme. Our first scheme does not place any constraints on the amount of space available on the limited-capacity storage device, and searches for the best representation that meets the requirements. The second scheme, on the other hand, has (modest) requirements on the storage space available, but guarantees a low rate of false positives: with O(mc) storage space available on the smartcard (where m is the number of items or groups of items included in the subscription and c is a selectable parameter), it achieves a rate of false positives of m −c .