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A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses., Comment: JEL Classification: C58, C63, G18

With a spatial statistical database covering a large region, how to publish differential privacy protected information is a challenge. In previous works, information was published using large fixed spatial cells. In this paper, we develop novel flexible methods to publish the spatial information, which allows the users to freely move around the large region, zoom in and zoom out at arbitrary locations, and obtain information over spatial areas both large and small. We develop two methods to publish the spatial information protected under differential privacy. First the region is divided into the smallest spatial cells, where each cell does not observe an event happening more than once. Given repeated measurements, such as multiple day data, the noise added Bernoulli probabilities are computed for all the smallest spatial cells. For larger spatial cells of high interests to users, the noisy Bernoulli probabilities are combined into noisy Poisson-Binomial distributions which also satisfy differential privacy requirement. We use the New York Taxi data in the experiments to demonstrate how our methods work. We show that both of our methods are accurate, while the noisy count probabilities directly obtained from fixed large spatial cells often generate the spatial counts much smaller than the true values.

AT1 antagonists is the most recent drug class of molecules against hypertension and they mediate their actions through blocking detrimental effects of angiotensin II (A-II) when acts on type I (AT1) A-II receptor. The effects of AT1 antagonists are not limited to cardiovascular diseases. AT1 receptor blockers may be used as potential anti-cancer agents - due to the inhibition of cell proliferation stimulated by A-II. Therefore, AT1 receptors and the A-II biosynthesis mechanisms are targets for the development of new synthetic drugs and therapeutic treatment of various cardiovascular and other diseases. In this work, multi-scale molecular modeling approaches were performed and it is found that oxazolone and imidazolone derivatives reveal similar/better interaction energy profiles compared to the FDA approved sartan molecules at the binding site of the AT1 receptor. In silico-guided designed hit molecules were then synthesized and tested for their binding affinities to human AT1 receptor in radioligand binding studies, using [I-125-Sar(1)-Ile(8)] AngII. Among the compounds tested, 19d and 9j molecules bound to receptor in a dose response manner and with relatively high affinities. Next, cytotoxicity and wound healing assays were performed for these hit molecules. Since hit molecule 19d led to deceleration of cell motility in all three cell lines (NIH3T3, A549, and H358) tested in this study, this molecule is investigated in further tests. In two cell lines (HUVEC and MCF-7) tested, 19d induced G2/M cell cycle arrest in a concentration dependent manner. Adherent cells detached from the plates and underwent cell death possibly due to apoptosis at 19d concentrations that induced cell cycle arrest. (C) 2017 Elsevier Masson SAS. All rights reserved.

Inference problem has always been an important and challenging topic of data privacy in databases. In relational databases, the traditional solution to this problem was to define views on relational schemas to restrict the subset of attributes and operations available to the users in order to prevent unwanted inferences. This method is a form of decomposition strategy, which mainly concentrates on the granularity of the accessible fields to the users, to prevent sensitive information inference. Nowadays, due to increasing data sharing among parties, the possibility of constructing complex indirect methods to obtain sensitive data has also increased. Therefore, we need to not only consider security threats due to direct access to sensitive data but also address indirect inference channels using functional and probabilistic dependencies (e.g., deducing gender of an individual from his/her name) while creating security views. In this paper, we propose a proactive and decomposition based inference control strategy for relational databases to prevent direct or indirect inference of private data. We introduce a new kind of context dependent attribute policy rule, which is named as security dependent set, as a set of attributes whose association should not be inferred. Then, we define a logical schema decomposition algorithm that prevents inference among attributes in security dependent set. The decomposition algorithm takes both functional and probabilistic dependencies into consideration in order to prevent all kinds of known inferences of relations among the attributes of security dependent sets. We prove that our proposed decomposition algorithm generates a secure logical schema that complies with the given security dependent set constraints. Since our proposed technique is purely proactive, it does not require any prior knowledge about executed queries and do not need to modify any submitted queries. It can also be embedded into any relational database management system without changing anything in the underlying system. We empirically compare our proposed method with the state of art reactive methods. Our extensive experimental analysis, conducted using TPC-H 1 benchmark scheme, shows the effectives our proposed approach.

Display Omitted We present a privacy-preserving protocol to detect concurrent trial participants.We present a name representation scheme resilient to frequency attacks.The accuracy of the protocol is similar to standard non-secure methods.For a database size of 20,000, the private query time is under 40s on 32 cores. ObjectiveSome phase 1 clinical trials offer strong financial incentives for healthy individuals to participate in their studies. There is evidence that some individuals enroll in multiple trials concurrently. This creates safety risks and introduces data quality problems into the trials. Our objective was to construct a privacy preserving protocol to track phase 1 participants to detect concurrent enrollment. DesignA protocol using secure probabilistic querying against a database of trial participants that allows for screening during telephone interviews and on-site enrollment was developed. The match variables consisted of demographic information. MeasurementThe accuracy (sensitivity, precision, and negative predictive value) of the matching and its computational performance in seconds were measured under simulated environments. Accuracy was also compared to non-secure matching methods. ResultsThe protocol performance scales linearly with the database size. At the largest database size of 20,000 participants, a query takes under 20s on a 64 cores machine. Sensitivity, precision, and negative predictive value of the queries were consistently at or above 0.9, and were very similar to non-secure versions of the protocol. ConclusionThe protocol provides a reasonable solution to the concurrent enrollment problems in phase 1 clinical trials, and is able to ensure that personal information about participants is kept secure.

Background: The effect of the timing of the second laparotomy on wound healing is not clear. In an experimental study in rats, we aimed to investigate the effect of timing on wound healing after reoperations on the same surgical site. Material and methods: Forty-eight rats were divided into four groups. The control group (GC) didn't have another laparotomy whereas the relaparotomies on the same surgical site were performed either on the 3rd, 15th or the 30th postoperative days in the three study groups (G3, G15, G30 respectively). The midline tension pressure, collagen types I, III and, histological analysis were performed from the specimens in order to assess the wound healing and strength. Results: The tensile strength was the highest in GC and decreased gradually in G3, G15 and G30, the difference between the groups did not reach statistical significance. Higher collagen levels, increased fibrosis, and large defects were observed in relaparotomy groups than CG. The musculoaponeurotic gap was shortest in GC when compared to other three relaparotomy groups (P

Record linkage is the task of identifying records from disparate data sources that refer to the same entity. It is an integral component of data processing in distributed settings, where the integration of information from multiple sources can prevent duplication and enrich overall data quality, thus enabling more detailed and correct analysis. Privacy-preserving record linkage (PPRL) is a variant of the task in which data owners wish to perform linkage without revealing identifiers associated with the records. This task is desirable in various domains, including healthcare, where it may not be possible to reveal patient identity due to confidentiality requirements, and in business, where it could be disadvantageous to divulge customers' identities. To perform PPRL, it is necessary to apply string comparators that function in the privacy-preserving space. A number of privacy-preserving string comparators (PPSCs) have been proposed, but little research has compared them in the context of a real record linkage application. This paper performs a principled and comprehensive evaluation of six PPSCs in terms of three key properties: (1) correctness of record linkage predictions, (2) computational complexity, and (3) security. We utilize a real publicly-available dataset, derived from the North Carolina voter registration database, to evaluate the tradeoffs between the aforementioned properties. Among our results, we find that PPSCs that partition, encode, and compare strings yield highly accurate record linkage results. However, as a tradeoff, we observe that such PPSCs are less secure than those that map and compare strings in a reduced dimensional space.

Organizations, such as federally-funded medical research centers, must share de-identified data on their consumers to publicly accessible repositories to adhere to regulatory requirements. Many repositories are managed by third-parties and it is often unknown if records received from disparate organizations correspond to the same individual. Failure to resolve this issue can lead to biased (e.g., double counting of identical records) and underpowered (e.g., unlinked records of different data types) investigations. In this paper, we present a secure multiparty computation protocol that enables record joins via consumers' encrypted identifiers. Our solution is more practical than prior secure join models in that data holders need to interact with the third party one time per data submission. Though technically feasible, the speed of the basic protocol scales quadratically with the number of records. Thus, we introduce an extended version of our protocol in which data holders append k-anonymous features of their consumers to their encrypted submissions. These features facilitate a more efficient join computation, while providing a formal guarantee that each record is linkable to no less than k individuals in the union of all organizations' consumers. Beyond a theoretical treatment of the problem, we provide an extensive experimental investigation with data derived from the US Census to illustrate the significant gains in efficiency such an approach can achieve.

The secure multi-party computation (SMC) model provides means for balancing the use and confidentiality of distributed data. This is especially important in the field of privacy-preserving data mining (PPDM). Increasing security concerns have led to a surge in work on practical secure multi-party computation protocols. However, most are only proven secure under the semi-honest model, and security under this adversary model is insufficient for many PPDM applications. SMC protocols under the malicious adversary model generally have impractically high complexities for PPDM. We propose an accountable computing (AC) framework that enables liability for privacy compromise to be assigned to the responsible party without the complexity and cost of an SMC-protocol under the malicious model. We show how to transform a circuit-based semi-honest two-party protocol into a protocol satisfying the AC-framework. The transformations are simple and efficient. At the same time, the verification phase of the transformed protocol is capable of detecting any malicious behaviors that can be prevented under the malicious model.

The perturbation method has been extensively studied for privacy preserving data mining. In this method, random noise from a known distribution is added to the privacy sensitive data before the data is sent to the data miner. Subsequently, the data miner reconstructs an approximation to the original data distribution from the perturbed data and uses the reconstructed distribution for data mining purposes. Due to the addition of noise, loss of information versus preservation of privacy is always a trade off in the perturbation based approaches. The question is, to what extent are the users willing to compromise their privacy? This is a choice that changes from individual to individual. Different individuals may have different attitudes towards privacy based on customs and cultures. Unfortunately, current perturbation based privacy preserving data mining techniques do not allow the individuals to choose their desired privacy levels. This is a drawback as privacy is a personal choice. In this paper, we propose an individually adaptable perturbation model, which enables the individuals to choose their own privacy levels. The effectiveness of our new approach is demonstrated by various experiments conducted on both synthetic and real-world data sets. Based on our experiments, we suggest a simple but effective and yet efficient technique to build data mining models from perturbed data.

Introduction and Aim: Duodenal eosinophilia has been previously described in subsets of patients with functional dyspepsia1 (FD), non-celiac wheat sensitivity2 and celiac disease (CD)3. CD has varied clinical presentations, including symptoms of functional gastrointestinal disorders (FGIDs), FD and irritable bowel syndrome (IBS). We aimed to determine if duodenal eosinophilia is present in patients with CD presenting with dyspepsia. Methods: The celiac study population comprised 61 patients, 44 female, mean age 42, (range 17-74) years with a new presentation of subsequent biopsy proven coeliac disease to a single centre from 2003-13. A standard symptom questionnaire was documented for all patients. The control population (53 adults) presenting for endoscopy with no CD was drawn from the same centre with similar demographics for age and gender1. Duodenal biopsies from both groups were assessed for eosinophil counts/ mm2. Results: In patients with coeliac disease 37/61 (60%) had symptoms of a FGID, 9 FD, 17 IBS, 11 overlap FD and IBS. 15 (71% of FD) had early satiety (ES) symptoms, 19 with reflux symptoms. In controls 33/53 (64%) had symptoms of FD, 17 (51%) with ES, 21 IBS. Early satiety was an equally common presentation in celiac and control patients. IBS was more common in celiac patients (p= 0.01). See Table. Compared to a similar population from the same hospital, duodenal eosinophils are significantly raised in CD compared to a group without coeliac disease attending for endoscopy, mean 45/mm2 vs 65/mm2 p=0.005. Eosinophil counts were similar in control patients with symptoms of dyspepsia (mean 50/ mm2) and early satiety (ES) (mean 55/ mm2), compared to those with CD and dyspepsia (mean 47/ mm2), p=0.80 and CD and ES (mean 56/ mm2), p=0.87. In controls with no dyspepsia eosinophil counts were significantly lower (mean 40/ mm2) than those with CD and no dyspepsia (mean 70/ mm2), p=0.005. See Figure. Among control patients, 3 reported food allergies, 2 with ES all 3 with D2 eosinophilia. Conclusions: Duodenal eosinophilia in coeliac patients with dyspepsia and ES is of a similar level to patients without CD with FD and ES. As duodenal eosinophilia is described in non-celiac wheat sensitivity it is possible that eosinophils are a sign of wheat intolerance and suggests a wheat free diet may benefit certain FD patients if duodenal eosinophilia is present.