PPM-HDA: privacy-preserving and multifunctional health data aggregation with fault tolerance

Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users' health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA⁺) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA^⊕ as an extension of MHDA⁺ to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users' privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA⁺ is significantly reduced with the assistance of CSs. Our MHDA^⊕ scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.

Cloud security

Cloud computing systems and services have become major targets for cyberattackers. To provide strong protection of cloud platforms, infrastructure, hosted applications, and data stored in the cloud, we need to address the security issue from a range of perspectives-from secure data and application outsourcing, to anonymous communication, to secure multiparty computation. This special issue on cloud security aims to address the importance of protecting and securing cloud platforms, infrastructures, hosted applications, and data storage.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, November 2016

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, November 2016

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

B-1 Monthly Report of Medical Services Provided under Title XIX of the Social Security Act, December 2016

B-1 Medicaid Reports -- The monthly Medicaid series of eight reports provide summaries of Medicaid eligibles, recipients served, and total payments by county, category of service, and aid category. These reports may also be known as the B-1 Reports. These reports are each available as a PDF for printing or as a CSV file for data analysis. Report Report name IAMM1800-R001--Medically Needy by County - No Spenddown and With Spenddown; IAMM1800-R002--Total Medically Needy, All Other Medicaid, and Grand Total by County; IAMM2200-R002--Monthly Expenditures by Category of Service; IAMM2200-R003--Fiscal YTD Expenditures by Category of Service; IAMM3800-R001--ICF & ICF-MR Vendor Payments by County; IAMM4400-R001--Monthly Expenditures by Eligibility Program; IAMM4400-R002--Monthly Expenditures by Category of Service by Program; IAMM4600-R002--Elderly Waiver Summary by County.

Assessing mackerel scad, Decapterus macarellus, in Cape Verde: using a Bayesian approach to biomass dynamic modelling in a data-limited situation

The fisheries for mackerel scad, Decapterus macarellus, are particularly important in Cape Verde, constituting almost 40% of total catches at the peak of the fishery in 1997 and 1998 ( 3700 tonnes). Catches have been stable at a much lower level of about 2 100 tonnes in recent years. Given the importance of mackerel scad in terms of catch weight and local food security, there is an urgent need for updated assessment. Stock assessment was carried out using a Bayesian approach to biomass dynamic modelling. In order to tackle the problem of a non-informative CPUE series, the intrinsic rate of increase, r, was estimated separately, and the ratio B-0/X, initial biomass relative to carrying capacity, was assumed based on available information. The results indicated that the current level of fishing is sustainable. The probability of collapse is low, particularly in the short-term, and it is likely that biomass may increase further above B-msy, indicating a healthy stock level. It would appear that it is relatively safe to increase catches even up to 4000 tonnes. However, the marginal posterior of r was almost identical to the prior, indicating that there is relatively low information content in CPUE. This was also the case in relation to B-0/X There have been substantial increases in fishing efficiency, which have not been adequately captured by the measure used for effort (days or trips), implying that the results may be overly optimistic and should be considered preliminary. (c) 2006 Elsevier B.V. All rights reserved.

Batch Verification of Validity of Bids in Homomorphic E-auction

Bid opening in e-auction is efficient when a homomorphic secret sharing function is employed to seal the bids and homomorphic secret reconstruction is employed to open the bids. However, this high efficiency is based on an assumption: the bids are valid (e.g., within a special range). An undetected invalid bid can compromise correctness and fairness of the auction. Unfortunately, validity verification of the bids is ignored in the auction schemes employing homomorphic secret sharing (called homomorphic auction in this paper). In this paper, an attack against the homomorphic auction in the absence of bid validity check is presented and a necessary bid validity check mechanism is proposed. Then a batch cryptographic technique is introduced and applied to improve the efficiency of bid validity check.

High body mass index is not a barrier to physical activity: Analysis of international rugby players' anthropometric data

Recent data indicate that levels of overweight and obesity are increasing at an alarming rate throughout the world. At a population level (and commonly to assess individual health risk), the prevalence of overweight and obesity is calculated using cut-offs of the Body Mass Index (BMI) derived from height and weight. Similarly, the BMI is also used to classify individuals and to provide a notional indication of potential health risk. It is likely that epidemiologic surveys that are reliant on BMI as a measure of adiposity will overestimate the number of individuals in the overweight (and slightly obese) categories. This tendency to misclassify individuals may be more pronounced in athletic populations or groups in which the proportion of more active individuals is higher. This differential is most pronounced in sports where it is advantageous to have a high BMI (but not necessarily high fatness). To illustrate this point we calculated the BMIs of international professional rugby players from the four teams involved in the semi-finals of the 2003 Rugby Union World Cup. According to the World Health Organisation (WHO) cut-offs for BMI, approximately 65% of the players were classified as overweight and approximately 25% as obese. These findings demonstrate that a high BMI is commonplace (and a potentially desirable attribute for sport performance) in professional rugby players. An unanswered question is what proportion of the wider population, classified as overweight (or obese) according to the BMI, is misclassified according to both fatness and health risk? It is evident that being overweight should not be an obstacle to a physically active lifestyle. Similarly, a reliance on BMI alone may misclassify a number of individuals who might otherwise have been automatically considered fat and/or unfit.