Privacy preserving algorithms for newly emergent computing environments

Privacy preserving data usage ensures appropriate usage of data without compromising sensitive information. Data privacy is a primary requirement since customers' data is an asset to any organization and it contains customers' private information. Data seclusion cannot be a solution to keep data private. Data sharing as well as keeping data private is important for different purposes, e.g., company welfare, research, business etc. A broad range of industries where data privacy is mandatory includes healthcare, aviation industry, education system, federal law enforcement, etc.In this thesis dissertation we focus on data privacy schemes in emerging fields of computer science, namely, health informatics, data mining, distributed cloud, biometrics, and mobile payments. Linking and mining medical records across different medical service providers are important to the enhancement of health care quality. Under HIPAA regulation keeping medical records private is important. In real-world health care databases, records may well contain errors. Linking the error-prone data and preserving data privacy at the same time is very difficult. We introduce a privacy preserving Error-Tolerant Linking Algorithm to enable medical records linkage for error-prone medical records. Mining frequent sequential patterns such as, patient path, treatment pattern, etc., across multiple medical sites helps to improve health care quality and research. We propose a privacy preserving sequential pattern mining scheme across multiple medical sites. In a distributed cloud environment resources are provided by users who are geographically distributed over a large area. Since resources are provided by regular users, data privacy and security are main concerns. We propose a privacy preserving data storage mechanism among different users in a distributed cloud. Managing secret key for encryption is difficult in a distributed cloud. To protect secret key in a distributed cloud we propose a multilevel threshold secret sharing mechanism. Biometric authentication ensures user identity by means of user's biometric traits. Any individual's biometrics should be protected since biometrics are unique and can be stolen or misused by an adversary. We present a secure and privacy preserving biometric authentication scheme using watermarking technique. Mobile payments have become popular with the extensive use of mobile devices. Mobile applications for payments needs to be very secure to perform transactions and at the same time needs to be efficient. We design and develop a mobile application for secure mobile payments. To secure mobile payments we focus on user's biometric authentication as well as secure bank transaction. We propose a novel privacy preserving biometric authentication algorithm for secure mobile payments

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

Personal data broker instead of blockchain for students’ data privacy assurance

Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students’ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solution. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.Peer ReviewedPostprint (author's final draft

Privacy-preserving targeted advertising scheme for IPTV using the cloud

In this paper, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of viewers/subscribers, a content provider (IPTV), an advertiser, and a cloud server. To provide high quality directed advertising service, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are published on the cloud server periodically (e.g. weekly) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the cloud, are considered (trade) secrets and therefore are protected as well. The cloud is oblivious to the published data, the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with a so-called {\em trapdoor} by the IPTV, can query the cloud and utilize the query results. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is suitable for practical usage

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