Preliminary Report of the Researches in the Primary Crystallization Observed through the Viscosity Change

Cooling the melt of 90% bismuth alloy from 300℃ to various measuring temperatures at the rate of 3.5℃/min, the relation between draining time and holding duration at each temperature was investigated. It was recognized that almost all the primary crystallization occurred after holding it for ca. 10 minutes and reached the equilibrium state after holding it for 30 minutes. The influence of cooling velocity was also investigated. The cooling procedure even at a slower rate than 0.5℃/min was sufficient to crystallize primary crystals in an equilibrium state

Fundamental Research on the Flowabilities of Melts of Metals and Alloys. I : On the Flowabilities of Sn-Bi and Pb-Sn Systems

The reason for the superior flowability of a melt in the neighborhood of the eutectic composition in binary system has been investigated. The flowability is subjected to two factors, the primary crystallization and the eutectic crystallization. Both increase the viscosity of melt, but the effect of the latter was shown to be less than that of the former

On the Primary Crystallization of the System Sn-Bi

The viscosity change during the solidification of the alloy Sn-Bi was studied with the rotational viscosimeter in place of the capillary method used in the previous work. The size of primary crystal was also microscopically examined. It was found that the primary crystals were apt to solidify in small sizes, as the composition of the alloy approached the eutectic, or when the temperature at which the alloy was kept became low

PRUDEnce: A system for assessing privacy risk vs utility in data sharing ecosystems

Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data

A risk model for privacy in trajectory data

Time sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper, we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data and then, we show how the empirical evaluation of the privacy risk has a different trend in synthetic data describing random movements