Securing Data Provenance in the Cloud

Asghar, Muhammad Rizwan; Ion, Mihaela; Russello, Giovanni; Crispo, Bruno

doi:10.1007/978-3-642-27585-2_12

Cited by 29 publications

(16 citation statements)

References 18 publications

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“…Moreover, if data is shared with a contact and the contact writes data to the CSP, i.e., modifies, adds or deletes data, the user may want to ensure that the changes really originate from a certain contact (Asghar et al, 2012). On the other hand, when the user writes data, the user may want to certify that changes indeed originates from the user.…”

Section: Data Integrity and Provenancementioning

confidence: 99%

Privacy-preserving Data Sharing in Portable Clouds

Zeidler

Asghar

2016

Proceedings of the 6th International Conference on Cloud Computing and Services Science

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Abstract:Cloud storage is a cheap and reliable solution for users to share data with their contacts. However, the lack of standardisation and migration tools makes it difficult for users to migrate to another Cloud Service Provider (CSP) without losing contacts, thus resulting in a vendor lock-in problem. In this work, we aim at providing a generic framework, named PortableCloud, that is flexible enough to enable users to migrate seamlessly to a different CSP keeping all their data and contacts. To preserve privacy of users, the data in the portable cloud is concealed from the CSP by employing encryption techniques. Moreover, we introduce a migration agent that assists users in automatically finding a suitable CSP that can satisfy their needs.

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Section: Data Integrity and Provenancementioning

confidence: 99%

Privacy-preserving Data Sharing in Portable Clouds

Zeidler

Asghar

2016

Proceedings of the 6th International Conference on Cloud Computing and Services Science

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“…This project builds on the University of Auckland's current work [28]. We will combine previous results on proxy-encryption for simple search operations in a multiuser setting [29][30][31] with our latest work [28,[32][33][34][35][36][37][38] supporting complex matching operations and indexing extensions.…”

Section: Ra2: Protection Of Privacy Of Data During Processing and Stomentioning

confidence: 99%

“…Our idea is that combining several PHE solutions supporting a range of operations can provide enough computation power to be used in several sectors including finance, healthcare and government. We will build from our previous work based on proxy encryption and Elgamal crypto blocks [29,32,33,38].…”

Section: Project 4: Efficient Privacy and Utility Preserving Encryptionmentioning

confidence: 99%

STRATUS: Towards Returning Data Control to Cloud Users

Russello

Nelson

et al. 2015

Algorithms and Architectures for Parallel Processing

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When we upload or create data into the cloud or the web, we immediately lose control of our data. Most of the time, we will not know where the data will be stored, or how many copies of our files are there. Worse, we are unable to know and stop malicious insiders from accessing the possibly sensitive data. Despite being transferred across and within clouds over encrypted channels, data often has to be decrypted within the database for it to be processed. Exposing the data at some point in the cloud to a few privileged users is undoubtedly a vendorcentric approach, and hinges on the trust relationships data owners have with their cloud service providers. A recent example of the abuse of the trust relationship is the high-profile Edward Snowden case. In this paper, we propose a user-centric approach which returns data control to the data owners-empowering users with data provenance, transparency and auditability, homomorphic encryption, situation awareness, revocation, attribution and data resilience. We also cover key elements of the concept of user data control. Finally, we introduce how we attempt to address these issues via the New Zealand Ministry of Business Innovation and Employment (MBIE)-funded STRATUS (Security Technologies Returning Accountability, Trust and User-centric Services in the Cloud) research project.

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“…They suggested an anonymous authentication and cryptography approach for their security method. [12] proposed the protection mechanism for data provenance which apply an encrypted search to the data. The proposed scheme focuses on improving the searching and query method from the cloud user or regulatory to achieve data confidentiality.…”

Section: Related Workmentioning

confidence: 99%

“…This could cause huge problems because data provenance is used not only in forensic investigation, but also in legal proceeding and scientific collaborations. Trustworthy data provenance is needed in order to provide unforgeability and non-reputation in the data life cycle [12]. Therefore, in order to provide trustworthy data provenance, security elements such as confidentiality, integrity, privacy, and availability should be included in the model.…”

Section: Securing Data Provenancementioning

confidence: 99%

Achieving trust in cloud computing using secure data provenance

Saad

Jalil

Manaf

2014

2014 IEEE Conference on Open Systems (ICOS)

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Cloud computing is the next generation computer system which extends network architecture into dynamic and large scale capacity by using visualization techniques. Transparent and secure data provenance will enhance the level of trust in the Cloud. Recent literature on securing data provenance focuses only on providing partial part of security elements in their mechanisms, thus they could not provide full protection to the data provenance as a whole. This paper presents the provenance description and challenges in providing security assurances in the Cloud. The paper also proposes a novel trust model for data provenance in cloud computing. The model will be used for securing the transaction process of storing and accessing the data provenance. The paper describes in detail the flow process of the model to achieve a high level of trust in cloud services. The significance of this research is to enhance the level of trust in the cloud services via comprehensive trust model which consists all aspects of security elements in the model.

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Securing Data Provenance in the Cloud

Cited by 29 publications

References 18 publications

Privacy-preserving Data Sharing in Portable Clouds

Privacy-preserving Data Sharing in Portable Clouds

STRATUS: Towards Returning Data Control to Cloud Users

Achieving trust in cloud computing using secure data provenance

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