Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

One of the main problems faced by Data Warehouse designers is fragmentation.Several studies have proposed data mining-based horizontal fragmentation methods.However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model.One of the main problems faced by Data Warehouse designers is fragmentation.Several studies have proposed data mining-based horizontal fragmentation methods.However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model

Tournesol: Permissionless Collaborative Algorithmic Governance with Security Guarantees

Recommendation algorithms play an increasingly central role in our societies. However, thus far, these algorithms are mostly designed and parameterized unilaterally by private groups or governmental authorities. In this paper, we present an end-to-end permissionless collaborative algorithmic governance method with security guarantees. Our proposed method is deployed as part of an open-source content recommendation platform https://tournesol.app, whose recommender is collaboratively parameterized by a community of (non-technical) contributors. This algorithmic governance is achieved through three main steps. First, the platform contains a mechanism to assign voting rights to the contributors. Second, the platform uses a comparison-based model to evaluate the individual preferences of contributors. Third, the platform aggregates the judgements of all contributors into collective scores for content recommendations. We stress that the first and third steps are vulnerable to attacks from malicious contributors. To guarantee the resilience against fake accounts, the first step combines email authentication, a vouching mechanism, a novel variant of the reputation-based EigenTrust algorithm and an adaptive voting rights assignment for alternatives that are scored by too many untrusted accounts. To provide resilience against malicious authenticated contributors, we adapt Mehestan, an algorithm previously proposed for robust sparse voting. We believe that these algorithms provide an appealing foundation for a collaborative, effective, scalable, fair, contributor-friendly, interpretable and secure governance. We conclude by highlighting key challenges to make our solution applicable to larger-scale settings.Comment: 31 pages, 5 figure

The Role of Privacy Within the Realm of Healthcare Wearables\u27 Acceptance and Use

The flexibility and vitality of the Internet along with technological innovation have fueled an industry focused on the design of portable devices capable of supporting personal activities and wellbeing. These compute devices, known as wearables, are unique from other computers in that they are portable, specific in function, and worn or carried by the user. While there are definite benefits attributable to wearables, there are also notable risks, especially in the realm of security where personal information and/or activities are often accessible to third parties. In addition, protecting one’s private information is regularly an afterthought and thus lacking in maturity. These concerns are amplified in the realm of healthcare wearable devices. Users must weigh the benefits with the risks. This is known as the privacy calculus. Often, users will opt for the wearable device despite the heightened concern that their information may or will be disclosed. This is known as the privacy paradox. While past research focused on specific wearable technologies, such as activity trackers and smartphones, the paradox of disclosure despite concern for privacy has not been the primary focus, particularly in the realm of the manifestation of the paradox when it comes to the acceptance and use of healthcare wearable devices. Accordingly, the objective of the present research was to propose and evaluate a research model specifically oriented towards the role of privacy in the realm of healthcare-related wearables’ acceptance and use. The presented model is composed of sixteen constructs informed from multiple theories including multiple technology acceptance theories, the Protection Motivation Theory (PMT), the Health Belief Model (HBM), and multiple privacy calculus theories. Using a survey-oriented approach to collect data, relationships among privacy, health, and acceptance constructs were examined using SmartPLS with intentions to validate the posited hypotheses and determine the influence of the various independent variables on the intention to disclose and the intention to adopt healthcare-wearables. Of particular interest is the posited moderating effects of perceived health status on intention to disclose personal information. The research endeavor confirmed significant evidence of the cost/benefit decision process, aka the privacy calculus, that takes place when deciding whether or not to disclose personal information in the healthcare wearables space. Perceived privacy risk was negatively correlated to intention to disclose while hedonic motivation and performance expectancy were positively correlated to intention to disclose. Furthermore, significant evidence was discovered pertaining to the privacy paradox via the moderating role that perceived health status plays regarding the relationships between the constructs of perceived privacy risk and intention to disclose and hedonic motivation and intention to disclose. Intention to disclose was also found to have a significant positive influence on intention to adopt. Contributions include understanding and generalization in the healthcare wearables adoption knowledge space with a particular emphasis on the role of privacy, as well as practical implications for wearable manufacturers and users

The impact of security and privacy issues on data management in fog Computing

With the increased growth of the application domains of IoT and the associated volumes of data generation, IoT systems are complicated and have small storage and recycling capacity. The cloud, a primary IoT storage medium with countless benefits, is not ideal for processing real time IoT data without delays. Capacity of data generated by IoTs keep increasing rampantly with associated security risks and privacy-preserving problems. Therefore, privacy maintenance, confidentiality and integrity of user’s data, improved latency and bandwidth restrictions are some of the major respective challenges of cloud computing. Fog computing is therefore a novel paradigm and an extension of the cloud. Which aims to improve cloud efficiency by enabling IoTs to locally process data before cloud transmission. However, some of the issues present in cloud such as the establishment of connection between edge devices often raise security and privacy concerns are also inherent in fog. The goal of this study, however, is to look at the state of data management security and privacy in a fog computing environment by reviewing existing security frameworks and data privacy procedures. This study lays bare the security vulnerabilities that exist inside the fog environment, creating hazards to user data privacy and security, and in lieu of that, this study incorporates features of data in addition to the acquired facts and statistics. Privacy-preservation is key to the continued use of services within the context of internet usage, as a result respondents indicated that they were experienced internet users who have been using the internet and its associated resources for various purposes, however respondents neither agreed nor disagreed with the possibility of the tracking or monitoring of their usage of the internet. The perception of respondents influenced the usage of the internet and various computing devices