A survey of cryptographic approaches to securing big-data analytics in the cloud

Yakoubov, Sophia; Gadepally, Vijay; Schear, Nabíl; Shen, Emily; Yerukhimovich, Arkady

doi:10.1109/hpec.2014.7040943

Cited by 53 publications

(24 citation statements)

References 10 publications

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“…The other approach is described in [22], where the authors provide a client-side library that provides cryptographic protections for Accumulo data. In [26], the authors describe other methods for securing data that can be applied to this work. i) Enhancements: While the ingest performance currently meets the needs for a single node producing LPM events under an I/O intensive workload, one area of future work will explore ways to increase the ingest performance such that a single node can support multiple clients at once.…”

Section: Discussionmentioning

confidence: 99%

High-throughput ingest of data provenance records into Accumulo

Moyer

Gadepally

2016

2016 IEEE High Performance Extreme Computing Conference (HPEC)

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Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must be stored somewhere where it can be queried. This problem becomes even more challenging when considering a network of provenance-aware machines all collecting this metadata. In this paper, we investigate the use of D4M and Accumulo to support high-throughput data ingest of whole-system provenance data. We find that we are able to ingest 3,970 graph components per second. Centrally storing the provenance metadata allows us to build systems that can detect and respond to data integrity attacks that are captured by the provenance system.

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Section: Discussionmentioning

confidence: 99%

High-throughput ingest of data provenance records into Accumulo

Moyer

Gadepally

2016

2016 IEEE High Performance Extreme Computing Conference (HPEC)

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“…A few cryptography-based approaches, such as homomorphic encryption (HE), verifiable computation (VC), and secure multi-party computation (MPC) have been designed in order to provide secure big-data processing in the Cloud [17]. However, other approaches, such as Asymmetric Key Cryptography and trusted Public Key Infrastructure have been developed over the years in order to support privacy preservation in the spatio-temporal domain.…”

Section: Levelmentioning

confidence: 99%

Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases

et al. 2019

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The need to store massive volumes of spatio-temporal data has become a difficult task as GPS capabilities and wireless communication technologies have become prevalent to modern mobile devices. As a result, massive trajectory data are produced, incurring expensive costs for storage, transmission, as well as query processing. A number of algorithms for compressing trajectory data have been proposed in order to overcome these difficulties. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. In the context of this research work, we focus on both the privacy preservation and storage problem of spatio-temporal databases. To alleviate this issue, we propose an efficient framework for trajectories representation, entitled DUST (DUal-based Spatio-temporal Trajectory), by which a raw trajectory is split into a number of linear sub-trajectories which are subjected to dual transformation that formulates the representatives of each linear component of initial trajectory; thus, the compressed trajectory achieves compression ratio equal to M : 1. To our knowledge, we are the first to study and address k-NN queries on nonlinear moving object trajectories that are represented in dual dimensional space. Additionally, the proposed approach is expected to reinforce the privacy protection of such data. Specifically, even in case that an intruder has access to the dual points of trajectory data and try to reproduce the native points that fit a specific component of the initial trajectory, the identity of the mobile object will remain secure with high probability. In this way, the privacy of the k-anonymity method is reinforced. Through experiments on real spatial datasets, we evaluate the robustness of the new approach and compare it with the one studied in our previous work. established as trajectory or mobility mining [1]. Also, the technology of databases is evolving to support the querying and representation of the trajectory of moving objects (e.g., humans, animals, vehicles, natural phenomena). Hence, the main parts of trajectory data-mining include pre-processing, data management, query processing, trajectory data-mining tasks, and privacy protection [2].Real-life applications, such as the analysis of traffic congestion, intelligent transportation, animal immigration habits analysis, cellular communications, military applications, structural and environmental monitoring, disaster/rescue management, as well as remediation, Geographic Information Systems (GIS), Location-Based Services (LBS), and other domains have increased the interest in the area of trajectory data-mining and efficient management of spatio-temporal data.It should be noted that the explosive growth of social media has produced large-scale mobility datasets whose publication puts people's personal lives at severe risk. Indeed, users get used to sharing their most-visited or potentially sensitive locations, such as their home, workplace, and holiday locations that are easy to obtain through social media. No...

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“…Prior work has looked at various techniques to improve the confidentiality, integrity and availability of big data systems and an overview is provided in [4]. There are many existing cryptographic tools capable of performing some level of computation on encrypted data.…”

Section: Introductionmentioning

confidence: 99%

Computing on Masked Data to improve the security of big data

Gadepally

Hancock

Kaiser

et al. 2015

2015 IEEE International Symposium on Technologies for Homeland Security (HST)

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Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need for improving the security of data stored in such untrusted servers or databases. Advances in cryptographic techniques and database technologies provide the necessary security functionality but rely on a computational model in which the cloud is used solely for storage and retrieval. Much of big data computation and analytics make use of signal processing fundamentals for computation. As the trend of moving data storage and computation to the cloud increases, homeland security missions should understand the impact of security on key signal processing kernels such as correlation or thresholding. In this article, we propose a tool called Computing on Masked Data (CMD), which combines advances in database technologies and cryptographic tools to provide a low overhead mechanism to offload certain mathematical operations securely to the cloud. This article describes the design and development of the CMD tool.

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A survey of cryptographic approaches to securing big-data analytics in the cloud

Cited by 53 publications

References 10 publications

High-throughput ingest of data provenance records into Accumulo

High-throughput ingest of data provenance records into Accumulo

Storage Efficient Trajectory Clustering and k-NN for Robust Privacy Preserving Spatio-Temporal Databases

Computing on Masked Data to improve the security of big data

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