Protection of big data privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data.

The effect of resistance training on functional capacity and quality of life in individuals with high and low number of metabolic risk factors

OBJECTIVE—There are limited data on the effects of resistance training on the capacity to perform activities of daily living (ADLs) and quality of life (QoL) for individuals with a high number of metabolic risk factors (HiMF). In this study, we examined the effect of resistance training on the capacity to perform ADLs and QoL in individuals with HiMF and compared any benefits with individuals with a low number of metabolic risk factors (LoMF).

RESEARCH DESIGN AND METHODS—Fifty-five untrained individuals, aged 50.8 ± 6.5 years, were randomized to four groups: HiMF training (HiMFT), HiMF control, LoMF training (LoMFT), and LoMF control. At baseline and after 10 weeks of resistance training, participants underwent anthropometric measurements and assessments of aerobic power (Vo2peak), muscle strength, capacity to perform ADLs, and a self-perceived QoL questionnaire. A repeated-measures ANOVA was used to examine the effect of training over time among groups.

RESULTS—Training increased lean body mass in both HiMFT (P = 0.03) and LoMFT (P = 0.03) groups. Total fat content and Vo2peak improved in the LoMFT group only. Muscle strength improved in both training groups (P < 0.01). Time to complete ADLs was reduced by 8.8% in the LoMFT group (P < 0.01) and 9.7% in the HiMF group (P < 0.01). Only the HiMFT group reported improvement in QoL.

CONCLUSIONS— Resistance training improved muscle strength and the capacity to perform ADLs in individuals with HiMF and LoMF. Resistance training improved QoL for the HiMF group, and this result was independent of changes in body fat content or aerobic power. Longer training regimens may be needed to improve QoL in individuals with LoMF.