16,325 research outputs found
A Solution for Privacy-Preserving and Security in Cloud for Document Oriented Data (By Using NoSQL Database)
Cloud computing delivers massively scalable computing resources as a service with Internet based technologies those can share resources within the cloud users. The cloud offers various types of services that majorly include infrastructure as services, platform as a service, and software as a service and security as a services and deployment model as well. The foremost issues in cloud data security include data security and user privacy, data protection, data availability, data location, and secure transmission. In now day, preserving-privacy of data and user, and manipulating query from big-data is the most challenging problem in the cloud. So many researches were conducted on privacy preserving techniques for sharing data and access control; secure searching on encrypted data and verification of data integrity. This work included preserving-privacy of document oriented data security, user privacy in the three phases those are data security at rest, at process and at transit by using Full Homomorphic encryption and decryption scheme to achieve afore most mentioned goal. This work implemented on document oriented data only by using NoSQL database and the encryption/decryption algorithm such as RSA and Paillier’s cryptosystem in Java package with MongoDB, Apache Tomcat Server 9.1, Python, Amazon Web Service mLab for MongoDB as remote server. Keywords: Privacy-Preserving, NoSQL, MongoDB, Cloud computing, Homomorphic encryption/decryption, public key, private key, RSA Algorithm, Paillier’s cryptosystem DOI: 10.7176/CEIS/11-3-02 Publication date:May 31st 202
Secure and Reliable Data Outsourcing in Cloud Computing
The many advantages of cloud computing are increasingly attracting individuals and organizations to outsource their data from local to remote cloud servers. In addition to cloud infrastructure and platform providers, such as Amazon, Google, and Microsoft, more and more cloud application providers are emerging which are dedicated to offering more accessible and user friendly data storage services to cloud customers. It is a clear trend that cloud data outsourcing is becoming a pervasive service. Along with the widespread enthusiasm on cloud computing, however, concerns on data security with cloud data storage are arising in terms of reliability and privacy which raise as the primary obstacles to the adoption of the cloud. To address these challenging issues, this dissertation explores the problem of secure and reliable data outsourcing in cloud computing. We focus on deploying the most fundamental data services, e.g., data management and data utilization, while considering reliability and privacy assurance. The first part of this dissertation discusses secure and reliable cloud data management to guarantee the data correctness and availability, given the difficulty that data are no longer locally possessed by data owners. We design a secure cloud storage service which addresses the reliability issue with near-optimal overall performance. By allowing a third party to perform the public integrity verification, data owners are significantly released from the onerous work of periodically checking data integrity. To completely free the data owner from the burden of being online after data outsourcing, we propose an exact repair solution so that no metadata needs to be generated on the fly for the repaired data. The second part presents our privacy-preserving data utilization solutions supporting two categories of semantics - keyword search and graph query. For protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. We define and solve the challenging problem of privacy-preserving multi- keyword ranked search over encrypted data in cloud computing. We establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. We first propose a basic idea for keyword search based on secure inner product computation, and then give two improved schemes to achieve various stringent privacy requirements in two different threat models. We also investigate some further enhancements of our ranked search mechanism, including supporting more search semantics, i.e., TF × IDF, and dynamic data operations. As a general data structure to describe the relation between entities, the graph has been increasingly used to model complicated structures and schemaless data, such as the personal social network, the relational database, XML documents and chemical compounds. In the case that these data contains sensitive information and need to be encrypted before outsourcing to the cloud, it is a very challenging task to effectively utilize such graph-structured data after encryption. We define and solve the problem of privacy-preserving query over encrypted graph-structured data in cloud computing. By utilizing the principle of filtering-and-verification, we pre-build a feature-based index to provide feature-related information about each encrypted data graph, and then choose the efficient inner product as the pruning tool to carry out the filtering procedure
A study on information induced medication errors
The electronic health record (eHR) system has recently been considered one of the biggest advancements in healthcare services. A personally controlled electronic health record (PCEHR) system is proposed by the Australian government to make the health system more agile, secure, and sustainable. Although the PCEHR system claims the electronic health records can be controlled by the patients, healthcare professionals and database/system operators may assist in disclosing the patients’ eHRs for retaliation or other ill purposes. As the conventional methods for preserving the privacy of eHRs solely trust the system operators, these data are vulnerable to be exploited by the authorised personnel in an immoral/unethical way. Furthermore, issues such as the sheer number of eHRs, their sensitive nature, flexible access, and efficient user revocation have remained the most important challenges towards fine-grained, cryptographically enforced data access control. In this paper we propose a patient centric cloud-based PCEHR framework, which employs a homomorphic encryption technique in storing the eHRs. The proposed system ensures the control of both access and privacy of eHRs stored in the cloud database
A Privacy-Preserving Framework for Personally Controlled Electronic Health Record (PCEHR) System
The electronic health record (eHR) system has recently been considered one of the biggest advancements in healthcare services. A personally controlled electronic health record (PCEHR) system is proposed by the Australian government to make the health system more agile, secure, and sustainable. Although the PCEHR system claims the electronic health records can be controlled by the patients, healthcare professionals and database/system operators may assist in disclosing the patients’ eHRs for retaliation or other ill purposes. As the conventional methods for preserving the privacy of eHRs solely trust the system operators, these data are vulnerable to be exploited by the authorised personnel in an immoral/unethical way. Furthermore, issues such as the sheer number of eHRs, their sensitive nature, flexible access, and efficient user revocation have remained the most important challenges towards fine-grained, cryptographically enforced data access control. In this paper we propose a patient centric cloud-based PCEHR framework, which employs a homomorphic encryption technique in storing the eHRs. The proposed system ensures the control of both access and privacy of eHRs stored in the cloud database
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using
clouds to host data query services has become an appealing solution for the
advantages on scalability and cost-saving. However, some data might be
sensitive that the data owner does not want to move to the cloud unless the
data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of
cloud computing. We propose the RASP data perturbation method to provide secure
and efficient range query and kNN query services for protected data in the
cloud. The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to
provide strong resilience to attacks on the perturbed data and queries. It also
preserves multidimensional ranges, which allows existing indexing techniques to
be applied to speedup range query processing. The kNN-R algorithm is designed
to work with the RASP range query algorithm to process the kNN queries. We have
carefully analyzed the attacks on data and queries under a precisely defined
threat model and realistic security assumptions. Extensive experiments have
been conducted to show the advantages of this approach on efficiency and
security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201
Privacy in the Genomic Era
Genome sequencing technology has advanced at a rapid pace and it is now
possible to generate highly-detailed genotypes inexpensively. The collection
and analysis of such data has the potential to support various applications,
including personalized medical services. While the benefits of the genomics
revolution are trumpeted by the biomedical community, the increased
availability of such data has major implications for personal privacy; notably
because the genome has certain essential features, which include (but are not
limited to) (i) an association with traits and certain diseases, (ii)
identification capability (e.g., forensics), and (iii) revelation of family
relationships. Moreover, direct-to-consumer DNA testing increases the
likelihood that genome data will be made available in less regulated
environments, such as the Internet and for-profit companies. The problem of
genome data privacy thus resides at the crossroads of computer science,
medicine, and public policy. While the computer scientists have addressed data
privacy for various data types, there has been less attention dedicated to
genomic data. Thus, the goal of this paper is to provide a systematization of
knowledge for the computer science community. In doing so, we address some of
the (sometimes erroneous) beliefs of this field and we report on a survey we
conducted about genome data privacy with biomedical specialists. Then, after
characterizing the genome privacy problem, we review the state-of-the-art
regarding privacy attacks on genomic data and strategies for mitigating such
attacks, as well as contextualizing these attacks from the perspective of
medicine and public policy. This paper concludes with an enumeration of the
challenges for genome data privacy and presents a framework to systematize the
analysis of threats and the design of countermeasures as the field moves
forward
Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments
For the past decade, query processing on relational data has been studied
extensively, and many theoretical and practical solutions to query processing
have been proposed under various scenarios. With the recent popularity of cloud
computing, users now have the opportunity to outsource their data as well as
the data management tasks to the cloud. However, due to the rise of various
privacy issues, sensitive data (e.g., medical records) need to be encrypted
before outsourcing to the cloud. In addition, query processing tasks should be
handled by the cloud; otherwise, there would be no point to outsource the data
at the first place. To process queries over encrypted data without the cloud
ever decrypting the data is a very challenging task. In this paper, we focus on
solving the k-nearest neighbor (kNN) query problem over encrypted database
outsourced to a cloud: a user issues an encrypted query record to the cloud,
and the cloud returns the k closest records to the user. We first present a
basic scheme and demonstrate that such a naive solution is not secure. To
provide better security, we propose a secure kNN protocol that protects the
confidentiality of the data, user's input query, and data access patterns.
Also, we empirically analyze the efficiency of our protocols through various
experiments. These results indicate that our secure protocol is very efficient
on the user end, and this lightweight scheme allows a user to use any mobile
device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table
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