Cloud Privacy Research Articles

Privacy Preserving Query over Encrypted Multidimensional Massive Data in Cloud Storage

Cloud storage is widely used in massive data outsourcing, but how to efficiently query encrypted multidimensional data stored in an untrusted cloud environment remains a research challenge. We propose a high performance and privacy-preserving query (pLSH-PPQ) scheme over encrypted multidimensional data to address this challenge. In our scheme, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-Based Encryption (CP-ABE) policy is used to control access to the search results. Therefore, only the requester with the permission attribute can obtain correct secret keys to decrypt the data. Security analysis proves that the pLSH-PPQ scheme achieves data confidentiality and reserves the data owner’s privacy in a semi-trusted cloud. In addition, evaluations demonstrate that the pLSH-PPQ scheme can significantly reduce response time and provide high search efficiency without compromising on search quality.

Cloud Computing Security, Privacy and Forensics: Issues and Challenges Ahead

Cloud Computing Security, Privacy and Forensics: Issues and Challenges Ahead

An ID-Based Group Key Agreement Scheme for Controlling Access and Privacy in Cloud

An ID-Based Group Key Agreement Scheme for Controlling Access and Privacy in Cloud

Cloud-Based Electronic Health Record Applications are Essential to Expeditionary Patient Care

The Air Force often uses paper to document expeditionary medical records because its IT systems are not properly integrated. To solve this, the Air Force Medical Service (AFMS) and DoD must successfully migrate to a joint cloud based electronic health record (EHR) system. It must be centralized, always up, and easily accessed through a tablet or a smartphone. 83% of healthcare organizations are using cloud based applications today. The AFMS and DoD healthcare will not remain a high reliability organization unless cloud migration is successful. A successful cloud migration will enable all treatment to be electronically documented jointly and available across multiple echelons in the expeditionary continuum of care. If successful, the DoD will deliver the world’s first globally integrated cloud based expeditionary healthcare system. Qualitative and quantitative analysis in this research concludes that cloud migrations must properly address IT security and cloud privacy concerns. The DoD must reach out to software vendors to help them integrate security standards cheaply during product design. Success here will allow the use of innovative application development platforms such as Amazon Cloud as well as Commercial-Off-the-Shelf (COTS) software and telehealth applications.

A Review on Cloud Computing Security and Privacy in Service Oriented Architecture (SOA)

Cloud computing is powerful computing which is transformed the traditional computing and industries. Cloud is not a new concept. Many organizations like Google, Microsoft, and Amazon accelerate in developing this computing and provide the services for lots of users and storing the data through cloud now become a norm. But there are many issues that arrive to store the data in cloud. In this paper we review some securities issues and give a survey solution that have been done to minimize the security risk and describe future research work about all these risk that occur when data is stored in cloud computing.

A New Authentication and Homomorphic Encryption as a Service Model for Preserving Privacy in Clouds

The security as a Service (SECaaS) is a new model which provides security solution to users through Cloud Computing. The maturity of Cloud Computing services makes possible the use of the SECaaS model. This new model offers huge benefits to users, such as Authentication as a Service (AaaS) and Encryption as a Service (ENCaaS). So, it can offer more security features, since it uses the resources of Clouds and it’s connected to the different security policy databases. While SECaaS offers to cloud users and companies a multitude of security services, it still remains very limited and several aspects of security are not covered by this model, especially the part concerning the privacy. In addition, SECaaS is a new model that is not yet correctly deployed and it is not sufficiently solicited by companies. On the other side, Homomorphic encryption is considered as a good solution to ensure the privacy for users using the cloud services because it permits to make calculation on cipher text and data without decrypting them, but this solution suffer from many limitations such as the key size, the high latency and some serious performance problems. The main idea of this paper it’s to propose a new security model to preserving user’s privacy using homomorphic encryption while bypassing its limitations. So, This paper proposes a framework for Authentication and Homomorphic Encryption (A-HEaaS) based on security as a Service model which permits a secure access to the Cloud servers and the use of homomorphic encryption for calculations on encrypted data. The paper describes the design of our model and gives an implementation of our framework on medical Data.

Incorporating privacy in the design of cloud-based systems: a conceptual meta-model

PurposeThe purpose of this paper is to extend PriS (privacy safeguard), a privacy requirements engineering method for eliciting and modelling privacy requirements during system design, with the addition of privacy-aware cloud-based concepts to assist analysts to reason and model about privacy in cloud environments.Design/methodology/approachAn analysis of previous findings on the file of cloud privacy based on previous work has been conducted and a set of privacy-related concepts that need to be considered during privacy analysis for cloud-based systems have been revealed. These concepts were used for extending the conceptual model of PriS.FindingsThe main finding of the paper is the design of a new, novel conceptual model that assists analysts and designers in reasoning about privacy in cloud environments. A new template using the JSON (Javascript notation object) format has been introduced for better expressing the privacy requirements along with the related concepts presented through the conceptual model, thus letting the developers to better understand the findings during the design stage and better guide them to the implementation of the respective solution.Research limitations/implicationsThe design of a cloud-based process that will guide analysts in detail for eliciting and modelling the identified privacy-related requirements is the limitation and in parallel the next step of the specific work presented here.Practical implicationsThe conceptual model has been applied on a real case scenario regarding its efficiency on capturing and mapping all necessary concepts for assisting analysts proceed with the design of the privacy-aware system. The results were positive, all concepts were easy to use and totally understandable from the design team and the stakeholders and the use of the JSON template received very positive comments, especially from the developer’s team.Originality/valueThe paper presents a novel conceptual model for reasoning about privacy requirements in the cloud. The applicability of the proposed model has also been tested on a real case study.

Location Privacy in Mobile Edge Clouds: A Chaff-Based Approach

In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with a focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood detection, we consider both heuristic strategies that mimic the user’s mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy or its online variation can drive the eavesdropper’s tracking accuracy to zero when the user’s mobility is sufficiently random. We further propose extended strategies that utilize randomization to defend against an advanced eavesdropper aware of the strategy. The efficacy of our solutions is verified through both synthetic and trace-driven simulations.

Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud

Cloud computing provides enormous storage capacity and huge computation power. Cloud enables users to deploy data-intensive applications without substructure investment. Intermediate data are generated under the cloud applications and stored again in cloud. An opponent may analyse multiple intermediate data sets to access the privacy sensitive information.The proposed technique to have good privacy and utility trade off, joint entropy with adaptive optimization process is used to maintain the privacy in cloud. Here optimal entropy value process using adaptive particle swarm optimization (APSO) process. After get the optimal entropy database difference model was processed, Entropy and database difference ratio is taken as the evaluation matrices and is tested using various datasets. The technique is also compared to the existing PSO optimization process on privacy preservation in cloud. An adaption feature of Particle swarm optimization show that APSO enhances privacy and preserving cost of intermediate data sets can be significantly saved.

Secure Cloud based Privacy Preserving DataMinning Platform

The adoption of cloud environment for various application uses has led to security and privacy concern of user’s data. To protect user data and privacy on such platform is an area of concern. Many cryptography strategy has been presented to provide secure sharing of resource on cloud platform. These methods tries to achieve a secure authentication strategy to realize feature such as self-blindable access tickets, group signatures, anonymous access tickets, minimal disclosure of tickets and revocation but each one varies in realization of these features. Each feature requires different cryptography mechanism for realization. Due to this it induces computation complexity which affects the deployment of these models in practical application. Most of these techniques are designed for a particular application environment and adopt public key cryptography which incurs high cost due to computation complexity. To address these issues this work present an secure and efficient privacy preserving of mining data on public cloud platform by adopting party and key based authentication strategy. The proposed SCPPDM (Secure Cloud Privacy Preserving Data Mining) is deployed on Microsoft azure cloud platform. Experiment is conducted to evaluate computation complexity. The outcome shows the proposed model achieves significant performance interm of computation overhead and cost.

Privacy in cloud computing environments: a survey and research challenges

Definitely, cloud computing represents a real evolution in the IT world that provides many advantages for both providers and users. This new paradigm includes several services that allow data storage and processing. However, outsourcing data to the cloud raises many issues related to privacy concerns. In fact, for some organizations and individuals, data privacy present a crucial aspect of their business. Indeed, their sensitive data (health, finance, personal information, etc.) have a very important value, and any infringement of privacy can cause great loss in terms of money and reputation. Therefore, without considering privacy issues, the adoption of cloud computing can be discarded by large spectra of users. In this paper, we provide a survey on privacy risks and challenges for public cloud computing. We present and evaluate the main existing solutions that have made great progress in this area. To better address privacy concerns, we point out considerations and guidelines while giving the remained open issues that require additional investigation efforts to fulfill preserving and enhancing privacy in public cloud.

Cloud Computing and Privacy

Cloud Computing and Privacy

Preserving privacy in Cloud based applications using two-factor authentication (TOTP/WTP)

Preserving privacy in Cloud based applications using two-factor authentication (TOTP/WTP)

Cloud computing and security issue

Cloud computing has make the conceptual and structural basis for tomorrow’s computing. The World computing structure is rapidly moving towards cloud based architecture. While it is important to take advantages of could based computing by means of moving it in diversified sectors, the security aspects in a cloud based computing environment is most important. Cloud based services and service providers are being evolved which has resulted in a new business trend based on cloud technology. With the introduction of large number of cloud based services and geographically differ cloud service providers, sensitive information of different entities are normally stored in remote servers and locations with the possibilities of being exposed to unauthorized access in situations where the cloud servers storing those information are compromised. If security is not strong and consistent, the flexibility and advantages that cloud computing has to offer will have little credibility. In this paper security in cloud computing is elaborated in a way that covers security issues, concerns and challenges for Data Security in Cloud. This paper presents a review on the cloud computing concepts as well as security issues inherent within the context of cloud computing and cloud structure. KEYWORD: Threats to cloud confidentiality, cloud integrity, Cloud Security Alliance, cloud availability & cloud privacy.

A Framework for Protecting Users' Privacy in Cloud

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge The main focus is to to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

Security and privacy preserving approaches in the eHealth clouds with disaster recovery plan

Cloud computing was introduced as an alternative storage and computing model in the health sector as well as other sectors to handle large amounts of data. Many healthcare companies have moved their electronic data to the cloud in order to reduce in-house storage, IT development and maintenance costs. However, storing the healthcare records in a third-party server may cause serious storage, security and privacy issues. Therefore, many approaches have been proposed to preserve security as well as privacy in cloud computing projects. Cryptographic-based approaches were presented as one of the best ways to ensure the security and privacy of healthcare data in the cloud. Nevertheless, the cryptographic-based approaches which are used to transfer health records safely remain vulnerable regarding security, privacy, or the lack of any disaster recovery strategy. In this paper, we review the related work on security and privacy preserving as well as disaster recovery in the eHealth cloud domain. Then we propose two approaches, the Security-Preserving approach and the Privacy-Preserving approach, and a disaster recovery plan. The Security-Preserving approach is a robust means of ensuring the security and integrity of Electronic Health Records, and the Privacy-Preserving approach is an efficient authentication approach which protects the privacy of Personal Health Records. Finally, we discuss how the integrated approaches and the disaster recovery plan can ensure the reliability and security of cloud projects.

Achieving high performance and privacy-preserving query over encrypted multidimensional big metering data

With the proliferation of smart grids, traditional utilities are struggling to handle the increasing amount of metering data. Outsourcing the metering data to heterogeneous distributed systems has the potential to provide efficient data access and processing. In an untrusted heterogeneous distributed system environment, employing data encryption prior to outsourcing can be an effective way to preserve user privacy. However, how to efficiently query encrypted multidimensional metering data stored in an untrusted heterogeneous distributed system environment remains a research challenge. In this paper, we propose a high performance and privacy-preserving query (P2Q) scheme over encrypted multidimensional big metering data to address this challenge. In the proposed scheme, encrypted metering data are stored in the server of an untrusted heterogeneous distributed system environment. A Locality Sensitive Hashing (LSH) based similarity search approach is then used to realize the similarity query. To demonstrate utility of the proposed LSH-based search approach, we implement a prototype using MapReduce for the Hadoop distributed environment. More specifically, for a given query, the proxy server will return K top similar data object identifiers. An enhanced Ciphertext-Policy Attribute-based Encryption (CP-ABE) policy is then used to control access to the search results. Therefore, only the requester with an authorized query attribute can obtain the correct secret keys to retrieve the metering data. We then prove that the P2Q scheme achieves data confidentiality and preserves the data owner’s privacy in a semi-trusted cloud. In addition, our evaluations demonstrate that the P2Q scheme can significantly reduce response time and provide high search efficiency without compromising on search quality (i.e. suitable for multidimensional big data search in heterogeneous distributed system, such as cloud storage system).

Cloud privacy: an empirical study of 20 cloud providers' terms and privacy policies—Part I: Table A1

This article is an empirical study of the Terms of Service and Privacy Policies of 20 cloud providers, and it is provided in two parts. Our discussion focusses on how these 20 cloud providers treat various key rights that individuals have under data protection law in the EU either when they contract directly with a cloud provider or when they access cloud services through a business or institution, such as their employer. This article identifies some common approaches adopted by providers as well as certain mismatches between their various legal documents, and it highlights the advantages and disadvantages of various practices found in the study. Part I discusses individuals' right to have their personal data processed fairly and lawfully, the right to be informed about the collection of data, the specific purposes of processing, and the way their data may be shared with third parties. In Part II, published in the next issue, we discuss how these 20 cloud providers treat data disclosures to third parties, including law enforcement agencies and their attitude towards specific individual rights provided under the Data Protection Directive. We also discuss the providers' approaches to disputes arising out of the use of their cloud service and their approaches to compensation and indemnification. Finally, this article makes some suggestions for more effective transparency and redress options for individuals and concludes with a number of practical findings arising from the empirical review.

Wireless Body Sensor Networks

In this paper, the authors provide a detailed overview and technical discussion and analysis of the latest research trends in securing body sensor networks. The core of this work aims at: (1) identifying the resource limitations and energy challenges of this category of wireless sensor networks, (2) considering the life-critical applications and emergency contexts that are encompassed by body sensor network services, and (3) studying the effect of these peculiarities on the design and implementation of rigorous and efficient security algorithms and protocols. The survey discusses the main advancements in the design of body sensor network cryptographic services (key generation and management, authentication, confidentiality, integrity, and privacy) and sheds the light on the prominent developments achieved in the field of securing body sensor network data in Cloud computing architectures. The elastic virtualization mechanisms employed in the Cloud, as well as the lucrative computing and storage resources available, makes the integration of body sensor network applications, and Cloud platforms a natural choice that is packed with various security and privacy challenges. The work presented in this paper focuses on Cloud privacy and integrity mechanisms that rely on tamper-proof hardware and energy-efficient cryptographic data structures that are proving to be well-suited for operation in untrusted Cloud environments. This paper also examines two crucial design patterns that lie at the crux of any successful body sensor network deployment which are represented in: (1) attaining the right balance between the degree, complexity, span, and strength of the cryptographic operations employed and the energy resources they consume. (2) Achieving a feasible tradeoff between the privacy of the human subject wearing the body sensor network and the safety of this subject. This is done by a careful analysis of the medical status of the subject and other context-related information to control the degree of disclosure of sensitive medical data. The paper concludes by presenting a practical overview of the cryptographic support in the main body sensor network development frameworks such and TinyOS and SPINE and introduces a set of generalized guideline patterns and recommendations for designing and implementing cryptographic protocols in body sensor network environments.

Security and Privacy Concerns for Australian SMEs Cloud Adoption: Empirical Study of Metropolitan Vs Regional SMEs

New national infrastructure initiatives such as National Broadband Network (NBN) could enable Small and Medium-sized Enterprises (SMEs) in Australia to further embrace Cloud computing service. However, the ability of Cloud computing to store data remotely and share services in a dynamic environment bring along with it Cloud security and privacy concerns. In this study, we examined the influence of privacy and security factors on Cloud adoption by Australian SMEs through a questionnaire survey. Data was collected from 150 SMEs (i.e. 79 metropolitan SMEs and 71 regional SMEs) and structural equation modelling was used for data analysis. The findings show that Cloud privacy and security factors are not the most critical concern for Australian SMEs. Moreover, the results indicate that Cloud computing adoption is not influenced by the geographical location of the SMEs. This study extends the current understanding of Cloud computing adoption by Australian SMEs.