A historical probability based noise generation strategy for privacy protection in cloud computing

ZhangGaofeng,; YangYun,; ChenJinjun,

doi:10.1016/j.jcss.2011.12.020

Cited by 66 publications

(37 citation statements)

References 33 publications

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“…And a common model is presented in terms of obfuscation-based private web search [28]. Zhang et al [9] present a historical probability based noise generation strategy for privacy protection in cloud computing to obtain a promising cost-saving in cloud environments. Besides, a similar idea about packet padding [25] focuses on concealing fingerprints of web pages.…”

Section: Related Workmentioning

confidence: 99%

“…Our time-series pattern based noise injection model is modified from [9] to fulfill our time-series pattern idea as shown in Fig. 3.…”

Section: Noise Injection Modelmentioning

confidence: 99%

“…Currently, a historical probability based noise generation strategy (HPNGS) has been proposed to reduce the cost of noise obfuscation on cloud [9]. Compared to conventional random noise generation [10], HPNGS generates noise requests based on historical probabilities, and final requests including noise ones and real ones can reach about the same occurrence probabilities, with far fewer noise requests.…”

Section: Introductionmentioning

confidence: 99%

“…In such cases, the 'Sydney' privacy can be protected by noise obfuscation in general. One approach for improving this process is to decrease noise requests as in [9], under the pay-as-you-go style of cloud computing. But, given the privacy risk identified in this paper earlier, the customer could go to 'Sydney' in this month and 'Perth' in the next month.…”

Section: Introductionmentioning

confidence: 99%

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Time-Series Pattern Based Effective Noise Generation for Privacy Protection on Cloud

Zhang

Liu

Yang

2015

IEEE Trans. Comput.

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Abstract-Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service providers may exist in these cloud environments, and some of them may record service data from a customer and then collectively deduce the customer's private information without permission. Therefore, from the perspective of cloud customers, it is essential to take certain technical actions to protect their privacy at client side. Noise obfuscation is an effective approach in this regard by utilizing noise data. For instance, noise service requests can be generated and injected into real customer service requests so that malicious service providers would not be able to distinguish which requests are real ones if these requests' occurrence probabilities are about the same, and consequently related customer privacy can be protected. Currently, existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case, the probability fluctuation could not be concealed by existing noise generation strategies, and it is a serious risk for the customer's privacy. To address this probability fluctuation privacy risk, we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First, we analyze this privacy risk and present a novel cluster based algorithm to generate time intervals dynamically. Then, based on these time intervals, we investigate corresponding probability fluctuations and propose a novel time-series pattern based forecasting algorithm. Lastly, based on the forecasting algorithm, our novel noise generation strategy can be presented to withstand the probability fluctuation privacy risk. The simulation evaluation demonstrates that our strategy can significantly improve the effectiveness of such cloud privacy protection to withstand the probability fluctuation privacy risk.

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Section: Related Workmentioning

confidence: 99%

“…Our time-series pattern based noise injection model is modified from [9] to fulfill our time-series pattern idea as shown in Fig. 3.…”

Section: Noise Injection Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-Series Pattern Based Effective Noise Generation for Privacy Protection on Cloud

Zhang

Liu

Yang

2015

IEEE Trans. Comput.

Self Cite

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“…Gaofeng mentioned that the use of cloud computing by criminals means that their devices will be virtualized, geographically distributed and ephemeral, presenting technical and jurisdictional challenges for their identification and seizure by law enforcement and national-security agencies. These can impede digital forensic investigators and potentially prevent law enforcement and national-security agencies from acquiring digital evidence and analyzing digital content forensically in a timely fashion [9]. Other researchers such as Mell and Grance also mention that the sourcing and delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction infrastructure as a Service is a basic product, which can be offered by a cloud provider.…”

Section: A Taxonomy Of CCmentioning

confidence: 99%

A taxonomy of : The reviews on Cloud Computing Direction (CCD)

Yassin

Alnidawy²

2015

JACST

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The manufacturing industry is undergoing a major transformation enabled by IT and related smart technologies. The main thrust of Cloud computing is to provide on-demand computing services with high reliability, scalability and availability in a distributed environment. This paper summarizes our taxonomy of the CC review direction. The goals of this taxonomy were (i) clarify the needs and the directions of the use of the CC, (ii) define the academic and practical issues involved in CC , (iii) learn the state of the directions on methodologies of the CC , (iv) identify future research directions, which benefit the short and long terms. The taxonomy has concluded that (i) CC is advantageous in dealing with changes and uncertainties in the every-changing environment. (ii)It has been found that few existing CC can achieve the objective of security. (iii) The obstacles of the development of CC include the difficulties to identify and generalize the requirement of CC security, the lake of effective technologies that can be used to support the clouding use, and no international origination that serves for standardizing the modular components for cloud computing processes. In this paper, we use the IVSL(The Iraq Virtual Science Library) to select the free, full-text access to papers from major publishers as well as a large collection of on-line educational materials.

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A group‐choose algorithm supporting virtual organization creation for workflow deployment in cloud environment

Pan

2013

Concurrency and Computation

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Virtual organization (VO) is a main organizational paradigm for enterprises to collaborate in the rapidly changing environment. However, finding partners for VO creation has great challenges on Internet, because a VO initiator is blinded without enough partners' information. The problem of VO creation in cloud environment is discussed. A VO creating algorithm (called Group‐Choose) based on reputation system is presented to help initiator minimize the operating risk on Internet. In the algorithm, a VO initiator aggregates partners' trust evaluations of candidates to select new partners for VO, instead of evaluating candidate's trust only by itself. The third‐party assessment and Time Slide Window are used in peer‐to‐peer trust evaluation to make the model more adaptive in the dynamic environment. The proposed method focuses on the reputation data, without taking other factors into consideration. Compared with PathTrust and PeerTrust, the Group‐Choose algorithm for VO creation has better performance in resisting conspiracy attack and periodic attack. Copyright © 2013 John Wiley & Sons, Ltd.

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A historical probability based noise generation strategy for privacy protection in cloud computing

Cited by 66 publications

References 33 publications

Time-Series Pattern Based Effective Noise Generation for Privacy Protection on Cloud

Time-Series Pattern Based Effective Noise Generation for Privacy Protection on Cloud

A taxonomy of : The reviews on Cloud Computing Direction (CCD)

A group‐choose algorithm supporting virtual organization creation for workflow deployment in cloud environment

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