A Secure Clustering Algorithm for Distributed Data Streams

Täubner, Claudia; Mathiak, Brigitte; Eckstein, Silke

doi:10.1109/icdmw.2007.65

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…However, the achievements of scalability and fault-tolerance are still uncertain (transfer of the full database of a participant, encrypted, to the other participant, and use of random parts and SMC algorithms). The authors of [19] (which extends [17] and [18]) use similar SMC algorithms as well as random parts, and inherit from their drawbacks. The authors of [35] consider many more participants but (1) organize them in a rigid tree-like structure and use random parts and generic SMC techniques, which questions fault-tolerance and scalability, and (2) disclose the raw means, which raises security concerns.…”

Section: Related Workmentioning

confidence: 99%

“…Specific SMC algorithms address the problem of clustering horizontally partitioned datasets [5,17,18,19,20,22,26,27,35]. Most of them consider only two participants, choose k-means or a similar algorithm for performing the clustering, and all tackle the honest-but-curious attack model.…”

Section: Related Workmentioning

confidence: 99%

“…This problem is not addressed satisfactorily by related work. First, the common use of random parts or of generic secure multi-party computation techniques [5,17,18,19,20] severely questions their ability to scale with the number of participating devices and cope with arbitrary connections/disconnections. Second, the straightforward disclosure of data-dependent information proposed in some work, e.g., [22,35], jeopardizes their privacy guarantees.…”

Section: Figure 1: Collaborative Clustering Of Massive Personal Time-mentioning

confidence: 99%

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Chiaroscuro

Allard

Hébrail

Masséglia

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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The advent of on-body/at-home sensors connected to personal devices leads to the generation of fine grain highly sensitive personal data at an unprecendent rate. However, despite the promises of large scale analytics there are obvious privacy concerns that prevent individuals to share their personnal data. In this paper, we propose Chiaroscuro, a complete solution for clustering personal data with strong privacy guarantees. The execution sequence produced by Chiaroscuro is massively distributed on personal devices, coping with arbitrary connections and disconnections. Chiaroscuro builds on our novel data structure, called Diptych, which allows the participating devices to collaborate privately by combining encryption with differential privacy. Our solution yields a high clustering quality while minimizing the impact of the differentially private perturbation. Chiaroscuro is both correct and secure. Finally, we provide an experimental validation of our approach on both real and synthetic sets of time-series.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Figure 1: Collaborative Clustering Of Massive Personal Time-mentioning

confidence: 99%

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Chiaroscuro

Allard

Hébrail

Masséglia

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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“…This algorithm utilizes divide and conquer method to get closed weighted frequent items in data stream. [28] Introduced a protocol for efficiency in communication and data security. This protocol is based on an IACA making it appropriate to find center cluster than other algorithms like k-mean.…”

Section: Application Of Data Mining In Crime Detectionmentioning

confidence: 99%

“…Determine frequent items based on closed weight in DataStream. [28] How to keep distributed privacy for clustering of data streams.…”

Section: Time Granularity Adjustmentmentioning

confidence: 99%

Challenges and Issues in DATA Stream: A Review

Arif¹,

Alam²,

Hussain³

2015

IJHIT

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Data stream is a continuous, time varying, massive and infinitely ordered sequence of data elements. The streaming data are fast changing with time, it is impossible to acquire all the elements in a data stream. Therefore, each data element should be examined at most once in data streams. Memory usage for mining data stream should be limited due to the new data elements are continuously generated from the streams. It is essential to ensure that newly arrived stream should be immediately available whenever it is requested made this task much challenging and necessary for fraud detection in stream, taking out knowledge, for business improvement and other applications where data arrived in stream. This paper tries to highlight important issues and research challenges of data stream by means of a comprehensive review.

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Secure Adaptive Filtering

Troncoso-Pastoriza

Pérez-González

2011

IEEE Trans.Inform.Forensic Secur.

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A Secure Clustering Algorithm for Distributed Data Streams

Cited by 3 publications

References 13 publications

Chiaroscuro

Chiaroscuro

Challenges and Issues in DATA Stream: A Review

Secure Adaptive Filtering

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