A Legal Ontology to Support Privacy Preservation in Location-Based Services

Abstract. During the last years many laws have been promulgated in diverse countries to protect citizens ’ privacy. This fact is due to the increase of privacy threats caused by the tendency of using information technologies in all scopes. Location Based Services (LBS) compose a situation where this privacy can be harmed. Even there exist mechanisms to protect this right in LBS, generally this services have not been developed over regulatory norms or if so, it has been in a partial way or interpreting those norms in a particular form. This situation could be a consequence of the lack of a common knowledge base representing the actual legislation in matters of privacy. In this paper an ontology of the main Spanish privacy norm is presented as well as the method used to construct it. The ontology is specifically aimed and applied to the preservation of privacy in LBS

Associations of body composition and physical fitness with gestational diabetes and cardiovascular health in pregnancy: Results from the HealthyMoms trial

The aim of this study was to examine associations of body composition (fat mass index, % fat mass, fat-free mass index, body mass index) and physical fitness (cardiorespiratory fitness and handgrip strength) with gestational diabetes and cardiovascular health in early pregnancy. This cross-sectional study utilized baseline data (n = 303) collected in early pregnancy from the HealthyMoms trial. Body composition was measured using air-displacement plethysmography, cardiorespiratory fitness was assessed by means of the 6-min walk test and handgrip strength using a dynamometer. Logistic regression was used to estimate odds ratios (ORs) for gestational diabetes as well as high (defined as 1 SD above the mean) blood pressure, homeostatic model assessment for insulin resistance (HOMA-IR), and metabolic syndrome score (MetS score) per 1 SD increase in body composition and fitness variables. Fat mass index, % fat mass and body mass index were all strongly associated with gestational diabetes (ORs: 1.72-2.14, P = 0.61). In conclusion, accurately measured fat mass index or % fat mass were strongly associated with gestational diabetes risk and markers of cardiovascular health although associations were not stronger than the corresponding ones for body mass index. Fat-free mass index had only weak associations with gestational diabetes and cardiovascular health which support that the focus during clinical care would be on excess fat mass and not fat-free mass.Peer reviewe

Preserving User Location Privacy in Mobile Data Management Infrastructures

Abstract. Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user’s location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.

A Methodological Assessment of Location Privacy Risks in Wireless Hotspot Networks

Abstract. Mobile computing enables users to compute and communicate almost regardless of their current location. However, as a side effect this technology considerably increased surveillance potential for user movements. Current research addresses location privacy rather patchwork-like than comprehensively. Thus, this paper presents a methodology for identifying, assessing, and comparing location privacy risks in mobile computing technologies. In a case study, we apply the approach to IEEE 802.11b wireless LAN networks and location-based services, where it reveals significant location privacy concerns through link- and application-layer information. From a technological perspective, we argue that these are best addressed through novel anonymity-based mechanisms.

Principles for Entity Authentication

We study the roles of message components in authentication protocols

A Formal Model of Obfuscation and Negotiation for Location Privacy

Abstract. Obfuscation concerns the practice of deliberately degrading the quality of information in some way, so as to protect the privacy of the individual to whom that information refers. In this paper, we argue that obfuscation is an important technique for protecting an individual’s location privacy within a pervasive computing environment. The paper sets out a formal framework within which obfuscated location-based services are defined. This framework provides a computationally efficient mechanism for balancing an individual’s need for high-quality information services against that individual’s need for location privacy. Negotiation is used to ensure that a location-based service provider receives only the information it needs to know in order to provide a service of satisfactory quality. The results of this work have implications for numerous applications of mobile and location-aware systems, as they provide a new theoretical foundation for addressing the privacy concerns that are acknowledged to be retarding the widespread acceptance and use of location-based services.

Predicting CEO Misbehavior from Observables: Comparative Evaluation of Two Major Personality Models

The primary purpose of this study is to demonstrate how publicly observable pieces of information can be used to build various psychological profiles that can be utilized for the prediction of behavior within a risk analysis framework. In order to evaluate the feasibility of the proposed method, publicly available interview data is processed from a sample of chief executive officers (CEOs) using the IBM Watson Personality Insights service. The hypothesis-that group membership gives rise to a specific selection bias-is investigated by analyzing the IBM Watson-derived personality profiles at the aggregate level. The profiles are represented by two major theories of motivation and personality: the Basic Human Values and the Big Five models. Both theories are evaluated in terms of their utility for predicting adverse behavioral outcomes. The results show that both models are useful for identifying group-level differences between (1) the sample of CEOs and the general population, and (2) between two groups of CEOs, when a history of rule-breaking behavior is considered. The predictive performance evaluation conducted on the current sample shows that the binary logistic regression model built from the Basic Human Values outperforms the Big Five model, and that it provides a practically more useful measurement of individual differences. These results contribute to the development of a risk analysis method within the domain of information security, which addresses human-related risks