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

Liu, Fang; Shu, Xiaokui; Yao, Danfeng; Butt, Ali R.

doi:10.1145/2699026.2699106

Cited by 44 publications

(19 citation statements)

References 38 publications

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“…Machine learning algorithms are also used in context‐based DLPD approaches . In the era of big data, the most severe problem of content‐based DLPD approaches is scalability, i.e., they are not able to process massive content data in time …”

Section: Dlpd Techniquesmentioning

confidence: 99%

“…To address the above challenges, we now introduce a privacy preserving data leak detection system as a case study, named MapReduce‐based Data Leak Detection (MR‐DLD) . It utilizes the MapReduce distributed computing framework to inspect sensitive content for inadvertent data leak detection, and can be deployed either in local computer clusters or in the Cloud.…”

Section: Dlpd In the Big Data Eramentioning

confidence: 99%

See 1 more Smart Citation

Enterprise data breach: causes, challenges, prevention, and future directions

Cheng

Liu

Yao

2017

WIREs Data Min & Knowl

Self Cite

189

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A data breach is the intentional or inadvertent exposure of confidential information to unauthorized parties. In the digital era, data has become one of the most critical components of an enterprise. Data leakage poses serious threats to organizations, including significant reputational damage and financial losses. As the volume of data is growing exponentially and data breaches are happening more frequently than ever before, detecting and preventing data loss has become one of the most pressing security concerns for enterprises. Despite a plethora of research efforts on safeguarding sensitive information from being leaked, it remains an active research problem. This review helps interested readers to learn about enterprise data leak threats, recent data leak incidents, various state-of-the-art prevention and detection techniques, new challenges, and promising solutions and exciting opportunities.

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Section: Dlpd Techniquesmentioning

confidence: 99%

Section: Dlpd In the Big Data Eramentioning

confidence: 99%

Enterprise data breach: causes, challenges, prevention, and future directions

Cheng

Liu

Yao

2017

WIREs Data Min & Knowl

Self Cite

189

View full text Add to dashboard Cite

show abstract

“…The ElGamal cryptographic approach has been used in distributed fashion where semi-honest server mine the encrypted data. Liu et al [2] proposed privacy preserved scanning of big data using MapReduce framework. The technique minimizes the sensitive data exposure during the data detection for outsourcing data securely.…”

Section: Related Workmentioning

confidence: 99%

“…These voluminous datasets facilitate the analysis and understanding of much needed global trends and interesting patterns, for which organizations/clients may require to share their data with others. Sharing may cause exposure of sensitive information present in these datasets and might invite number of privacy threats [2] (e.g. medical records or financial records if mined can provide significant human benefits but the failure of privacy might allow malicious users or providers to misuse this information which can cause considerable economic or social loss).…”

Section: Introductionmentioning

confidence: 99%

Scalable two-phase co-occurring sensitive pattern hiding using MapReduce

Sharma

Toshniwal

2017

J Big Data

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Background: Expansion of Internet and its use for on-line activities such as E-Commerce and social networking are producing large volumes of transactional data. This huge data volume resulted from these activities facilitates the analysis and understanding of global trends and interesting patterns used for several decisive purposes. Analytics involved in these processes expose sensitive information present in these datasets, which is a serious privacy threat. To overcome this challenge, few sequential heuristics have been used in past where volumes of data were comparatively accommodating to these sequential heuristics; the current situation is not that much in-line and often results in high execution time. This new challenge of scalability paves a way for experimenting with Big Data approaches (e.g., MapReduce Framework). We have agglomerated the MapReduce framework with adopted heuristics to overcome this challenge of scalability along with much-needed privacy preservation and yields efficient analytic results within bounded execution times. Methods:MapReduce is a parallel programming framework [16] which provides us the opportunity to leverage largely distributed resources to deal with the Big Data analytics. MapReduce allows the resource of a largely distributed system to be utilized in a parallel fashion. The simplicity and high fault-tolerance are the key features which make MapReduce a promising framework. Therefore, we have proposed a two-phase MapReduce version of these adopted heuristics. MapReduce framework divides the whole data into 'n' number of data chunks D = {d 1 d ∪ 2 ∪ d 3 ..... ∪ d n } and distributes them over 'n' computing nodes to achieve the parallelization. The first phase of MapReduce job runs on each data chunk in order to generate intermediate results, which are further sorted and merged in the second phase to generate final sanitized dataset. Results:We conducted three set of experiments, each with five different scenarios corresponding to the different cluster sizes i.e., n = 1,2,3,4,5 where 'n' is a number of computing nodes. We compared the approaches with respect to real as well as synthetically generated large datasets. For varying data sizes and varying number of computing nodes, it has been observed that sanitization time required by the MapReduce-based algorithm for same size dataset is much less than the sequential traditional approach. Further, the scalability can be improved by using more number of computing nodes. Lastly, another set of experiments explores the change in sanitization time with varying sizes of the sensitive content present in a dataset. We evaluated the effectiveness of proposed approach in different scenarios, with varying cluster size from 1 to 5 nodes. It has been observed that still the execution time of our approach is much less than traditional schemes. Further, no hiding failure, artifactual patterns Sharma and Toshniwal J Big Data (2017) 4:4 DOI 10.1186/s40537-017-0064-9 Page 2 of 18 Sharma and Toshniwal J Big Data (2017) 4:4 ha...

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“…The two assumptions can be removed when our 2 Such channels are widely used for advanced NIDS where MITM (man-in-the-middle) SSL sessions are employed to handle encryption. alignment is performed utilizing secure multi-party computation or other privacy-preserving techniques [2,4]. We do not aim at detecting stealthy data leaks that an attacker encrypt the sensitive data by herself before leaking it.…”

Section: Models and Overviewmentioning

confidence: 99%

Rapid Screening of Transformed Data Leaks with Efficient Algorithms and Parallel Computing

Shu

Zhang

Yao

et al. 2015

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

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The leak of sensitive data on computer systems poses a serious threat to organizational security. Organizations need to identify the exposure of sensitive data by screening the content in storage and transmission, i.e., to detect sensitive information being stored or transmitted in the clear. However, detecting the exposure of sensitive information is challenging due to data transformation in the content. Transformations (such as insertion, deletion) result in highly unpredictable leak patterns. Existing automata-based string matching algorithms are impractical for detecting transformed data leaks, because of its formidable complexity when modeling the required regular expressions. We design two new algorithms for detecting long and transformed data leaks. Our system achieves high detection accuracy in recognizing transformed leaks compared to the state-of-the-art inspection methods. We parallelize our prototype on graphics processing unit and demonstrate the strong scalability of our detection solution required by a sizable organization.

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

Cited by 44 publications

References 38 publications

Enterprise data breach: causes, challenges, prevention, and future directions

Enterprise data breach: causes, challenges, prevention, and future directions

Scalable two-phase co-occurring sensitive pattern hiding using MapReduce

Rapid Screening of Transformed Data Leaks with Efficient Algorithms and Parallel Computing

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