Privacy and Cloud Computing in Public Schools

Today, data driven decision-making is at the center of educational policy debates in the United States. School districts are increasingly turning to rapidly evolving technologies and cloud computing to satisfy their educational objectives and take advantage of new opportunities for cost savings, flexibility, and always-available service among others. As public schools in the United States rapidly adopt cloud-computing services, and consequently transfer increasing quantities of student information to third-party providers, privacy issues become more salient and contentious. The protection of student privacy in the context of cloud computing is generally unknown both to the public and to policy-makers. This study thus focuses on K-12 public education and examines how school districts address privacy when they transfer student information to cloud computing service providers. The goals of the study are threefold: first, to provide a national picture of cloud computing in public schools; second, to assess how public schools address their statutory obligations as well as generally accepted privacy principles in their cloud service agreements; and, third, to make recommendations based on the findings to improve the protection of student privacy in the context of cloud computing. Fordham CLIP selected a national sample of school districts including large, medium and small school systems from every geographic region of the country. Using state open public record laws, Fordham CLIP requested from each selected district all of the district’s cloud service agreements, notices to parents, and computer use policies for teachers. All of the materials were then coded against a checklist of legal obligations and privacy norms. The purpose for this coding was to enable a general assessment and was not designed to provide a compliance audit of any school district nor of any particular vendor.https://ir.lawnet.fordham.edu/clip/1001/thumbnail.jp

Privacy and Cloud Computing in Public Schools

Today, data driven decision-making is at the center of educational policy debates in the United States. School districts are increasingly turning to rapidly evolving technologies and cloud computing to satisfy their educational objectives and take advantage of new opportunities for cost savings, flexibility, and always-available service among others. As public schools in the United States rapidly adopt cloud-computing services, and consequently transfer increasing quantities of student information to third-party providers, privacy issues become more salient and contentious. The protection of student privacy in the context of cloud computing is generally unknown both to the public and to policy-makers. This study thus focuses on K-12 public education and examines how school districts address privacy when they transfer student information to cloud computing service providers. The goals of the study are threefold: first, to provide a national picture of cloud computing in public schools; second, to assess how public schools address their statutory obligations as well as generally accepted privacy principles in their cloud service agreements; and, third, to make recommendations based on the findings to improve the protection of student privacy in the context of cloud computing. Fordham CLIP selected a national sample of school districts including large, medium and small school systems from every geographic region of the country. Using state open public record laws, Fordham CLIP requested from each selected district all of the district’s cloud service agreements, notices to parents, and computer use policies for teachers. All of the materials were then coded against a checklist of legal obligations and privacy norms. The purpose for this coding was to enable a general assessment and was not designed to provide a compliance audit of any school district nor of any particular vendor.https://ir.lawnet.fordham.edu/clip/1001/thumbnail.jp

Privacy and Cloud Computing in Public Schools

Today, data driven decision-making is at the center of educational policy debates in the United States. School districts are increasingly turning to rapidly evolving technologies and cloud computing to satisfy their educational objectives and take advantage of new opportunities for cost savings, flexibility, and always-available service among others. As public schools in the United States rapidly adopt cloud-computing services, and consequently transfer increasing quantities of student information to third-party providers, privacy issues become more salient and contentious. The protection of student privacy in the context of cloud computing is generally unknown both to the public and to policy-makers. This study thus focuses on K-12 public education and examines how school districts address privacy when they transfer student information to cloud computing service providers. The goals of the study are threefold: first, to provide a national picture of cloud computing in public schools; second, to assess how public schools address their statutory obligations as well as generally accepted privacy principles in their cloud service agreements; and, third, to make recommendations based on the findings to improve the protection of student privacy in the context of cloud computing. Fordham CLIP selected a national sample of school districts including large, medium and small school systems from every geographic region of the country. Using state open public record laws, Fordham CLIP requested from each selected district all of the district’s cloud service agreements, notices to parents, and computer use policies for teachers. All of the materials were then coded against a checklist of legal obligations and privacy norms. The purpose for this coding was to enable a general assessment and was not designed to provide a compliance audit of any school district nor of any particular vendor.https://ir.lawnet.fordham.edu/clip/1001/thumbnail.jp

Semantics of Data Mining Services in Cloud Computing

M. Parra-Royon holds a "Excelencia" scholarship from the Regional Government of Andaluc a (Spain). This work was supported by the Research Projects P12-TIC-2958 and TIN2016-81113-R (Ministry of Economy, Industry and Competitiveness - Government of Spain).In recent years with the rise of Cloud Computing (CC), many companies providing services in the cloud, are empowering a new series of services to their catalogue, such as data mining (DM) and data processing (DP), taking advantage of the vast computing resources available to them. Different service definition proposals have been put forward to address the problem of describing services in CC in a comprehensive way. Bearing in mind that each provider has its own definition of the logic of its services, and specifically of DM services, it should be pointed out that the possibility of describing services in a flexible way between providers is fundamental in order to maintain the usability and portability of this type of CC services. The use of semantic technologies based on the proposal offered by Linked Data (LD) for the definition of services, allows the design and modelling of DM services, achieving a high degree of interoperability. In this article a schema for the definition of DM services on CC is presented considering all key aspects of service in CC, such as prices, interfaces, Software Level Agreement (SLA), instances or DM work ow, among others. The new schema is based on LD, and it reuses other schemata obtaining a better and more complete definition of the services. In order to validate the completeness of the scheme, a series of DM services have been created where a set of algorithms such as Random Forest (RF) or KMeans are modeled as services. In addition, a dataset has been generated including the definition of the services of several actual CC DM providers, conforming the effectiveness of the schema.P12-TIC-2958 and TIN2016-81113-R (Ministry of Economy, Industry and Competitiveness - Government of Spain

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Secure Cloud Storage with Client-Side Encryption Using a Trusted Execution Environment

With the evolution of computer systems, the amount of sensitive data to be stored as well as the number of threats on these data grow up, making the data confidentiality increasingly important to computer users. Currently, with devices always connected to the Internet, the use of cloud data storage services has become practical and common, allowing quick access to such data wherever the user is. Such practicality brings with it a concern, precisely the confidentiality of the data which is delivered to third parties for storage. In the home environment, disk encryption tools have gained special attention from users, being used on personal computers and also having native options in some smartphone operating systems. The present work uses the data sealing, feature provided by the Intel Software Guard Extensions (Intel SGX) technology, for file encryption. A virtual file system is created in which applications can store their data, keeping the security guarantees provided by the Intel SGX technology, before send the data to a storage provider. This way, even if the storage provider is compromised, the data are safe. To validate the proposal, the Cryptomator software, which is a free client-side encryption tool for cloud files, was integrated with an Intel SGX application (enclave) for data sealing. The results demonstrate that the solution is feasible, in terms of performance and security, and can be expanded and refined for practical use and integration with cloud synchronization services