Data Leak Detection as a Service

Shu, Xiaokui; Yao, Danfeng

doi:10.1007/978-3-642-36883-7_14

Cited by 45 publications

(34 citation statements)

References 27 publications

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“…In [7], network-based data-leak detection (DLD) technique, have important feature of which is that the detection does not require the information proprietor to show the content of the sensitive data. Instead, only a small amount of specialize digests are needed.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Sensitive Data Coverage: Design and Analysis

Patel¹,

Nair²,

Shah³

2017

International Journal of Innovative Research in Electrical, Ele

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Privacy and security has become major issues in today's communication. In respect to the last ten years the nature and utilization of the communication technology is changed much frequently. Due to this a significant amount of data is communicated in a fraction of seconds. Therefore the traditional computational techniques have moved towards the big data processing and analytics. In this environment the entire client module and administration is directly connected with the same data sources. Due to this the communication becomes easy but security and privacy concern in communicated data has appeared. Sometimes these issues are arising due to data leakage. In this presented work the main aim is to investigate the privacy and security concerns due to data leakage in big data environment. The main reason to utilize the big data is to demonstrate the real time system using twitter accounts to fetch and improve the sensitivity of data. During the investigation the promising approach is appeared where the sliding window and fuzzy logic based system is provided to analyze and reform the data. But this approach is found slow processing capability by which the system performance is affected. Due to this a new approach using the random walk technique is prepared by modification of existing system to enhance the resource consumption of the system.

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Section: Related Workmentioning

confidence: 99%

Privacy Preserving Sensitive Data Coverage: Design and Analysis

Patel¹,

Nair²,

Shah³

2017

International Journal of Innovative Research in Electrical, Ele

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“…However, organizing of data across hundreds or thousands of servers becomes considerably additional challenging after a performance standpoint. Substantial quantities of data must be transported to each newly created VM [1].Data distribution methods should focus on reducing the overall transmission time. Dropping the total burden of data relocate around the mitigation that reduces the collision of organized service such a service on be the demand to take time [2].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Meta Scheduling based Virtual Consolidation for Resource Sharing in Green Cloud

Prabha¹,

Dhivya²

2016

BIJDM

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“…The work by Papadimitriou and Garcia-Molina [26] aims at finding the agents that leaked the sensitive data. Shu and Yao [32] presented privacy-preserving methods for protecting sensitive data in a non-MapReduce based detection environment. Shu et al [33] further proposed to accelerate screening transformed data leaks using GPU.…”

Section: Related Workmentioning

confidence: 99%

“…In the last category, the analysis proposed by Borders and Prakash [6] detects changes in network traffic patterns by searching for unjustifiable increase in HTTP traffic-flow volume, that indicates data exfiltration. The technique proposed by Shu and Yao [32] performs deep packet inspection to search for exposed outbound traffic that bears high similarity to sensitive data. Set intersection is used for the similarity measure.…”

mentioning

confidence: 99%

Privacy-Preserving Scanning of Big Content for Sensitive Data Exposure with MapReduce

Liu

Shu

Yao

et al. 2015

Proceedings of the 5th ACM Conference on Data and Application Security and Privacy

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The exposure of sensitive data in storage and transmission poses a serious threat to organizational and personal security. Data leak detection aims at scanning content (in storage or transmission) for exposed sensitive data. Because of the large content and data volume, such a screening algorithm needs to be scalable for a timely detection. Our solution uses the MapReduce framework for detecting exposed sensitive content, because it has the ability to arbitrarily scale and utilize public resources for the task, such as Amazon EC2. We design new MapReduce algorithms for computing collection intersection for data leak detection. Our prototype implemented with the Hadoop system achieves 225 Mbps analysis throughput with 24 nodes. Our algorithms support a useful privacy-preserving data transformation. This transformation enables the privacy-preserving technique to minimize the exposure of sensitive data during the detection. This transformation supports the secure outsourcing of the data leak detection to untrusted MapReduce and cloud providers.

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Data Leak Detection as a Service

Cited by 45 publications

References 27 publications

Privacy Preserving Sensitive Data Coverage: Design and Analysis

Privacy Preserving Sensitive Data Coverage: Design and Analysis

An Efficient Meta Scheduling based Virtual Consolidation for Resource Sharing in Green Cloud

Privacy-Preserving Scanning of Big Content for Sensitive Data Exposure with MapReduce

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