privacy preserving verifiable set operation in big data for cloud-assisted mobile crowdsourcing
TRANSCRIPT
CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249) MAIL ID: , [email protected]
Web: www.nexgenproject.com,
PRIVACY-PRESERVING VERIFIABLE SET OPERATION IN BIG DATA
FOR CLOUD-ASSISTED MOBILE CROWDSOURCING
ABSTRACT
The ubiquity of smartphones makes the mobile crowdsourcing possible, where the requester
(task owner) can crowd source data from the workers (smartphone users) by using their sensor-
rich mobile devices. However, data collection, data aggregation, and data analysis have become
challenging problems for a resource constrained requester when data volumeis extremely large,
i.e., big data. In particular to data analysis, set operations, including intersection, union, and
complementation, exists in most big data analysis for filtering redundant data and preprocessing
raw data. Facing challenges in terms of limited computation and storage resources, cloud-
assisted approaches may serve as a promising way to tackle big data analysis issue.However,
workers may not be willing to participate if the privacyof their sensing data and identity are not
well preserved in the untrusted cloud. In this work, we propose to use cloud to compute set
operation for the requester, at the same time workers’ data privacy and identities privacy are well
preserved. Besides, the requester can verify the correctness of set operation results. We also
extend our scheme to support data preprocessing, withwhich invalid data can be excluded before
data analysis. By usingbatch verification and data update methods, the proposed scheme greatly
reduces the computational cost. Extensive performance analysis and experiment-based on real
cloud system have shownboth the feasibility and efficiency of our proposed scheme.
EXISTING SYSTEM:
Private Set Intersection: Many works have been done toachieve private set intersection (PSI).
PSI enablestwo parties to compute the intersection with private inputand only the intersection is
known to each party. The firstprotocol for PSI is proposed . Kissner et al. usepolynomial
CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249) MAIL ID: , [email protected]
Web: www.nexgenproject.com,
representations to solve set operations between twoparties, and utilize Paillier Crypto system to
protect the privacyof polynomials when trusted third party is not available., private set
intersection with linear complexity isproposed. Dong et al make use of a new variant ofbloom
filter to achieve efficient PSI. , bloom filterand holomorphic encryption is used to achieved
outsourcedprivate set intersection. All of these works can achieve privateset intersection;
however, none of them offers verifiability ofthe result. Thus, none of them can be applied in our
workdirectly.Verifiable Computation: Verifiable computation was introduced and formalized by
Gennaro et al. , which enablesa resource- limited client to outsource the computation of afunction
to one or more workers. The workers return the resultof function evaluation. The client should be
able to efficientlyverify the correctness of the results. After that, many workshas been done to
achieve verifiable computation they propose the first practical verifiable computation scheme for
high degree polynomial functions. Fiore et al. propose a solution for publicly verifiable
computationof large polynomials and matrix computations, where anyonecan verify the
correctness of the results. Papamanthou et al. study the problem of cryptographically checkingthe
correctness of outsourced set operations performed by an untrusted server, and the sets are
dynamic.
PROPOSED SYSTEM:
we introduce the cloud intothe architecture as. The cloud serves as the intermediate entity
between the requester and the workers.When the requester wants to perform tasks over reported
datasets, she delegates the task to the cloud and waits for the result.Then, the cloud helps the
requester to collect all data setsfrom the workers and computes the set operation. However,this
solution may not work well because the public cloudis untrusted, and it may suffer severe
attacks, e.g., hackedby an adversary. On the one hand, in mobilecrowdsourcing, data privacy is a
big concern for the workers,for which sensitive data should not be revealed directly to thecloud.
In the above example, a worker is unwilling to exposeher travel destinations to the cloud because
this might breachher location privacy and cause physical attacks.On the other hand, security
CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249) MAIL ID: , [email protected]
Web: www.nexgenproject.com,
issues also exist in cloud-assisted set operation for mobile crowdsourcing. Crowdsourced
datamight be modified by an untrusted cloud if it knows a datacomes from a specific worker. An
untrusted cloud may returna wrong set operation result to the requester. When computingset
operations, the cloud may discard some data sets to reduceexpense. Facing these challenges, we
propose a verifiable setoperation in big data for cloud-assisted mobile crowdsourcing. Our
solution leverages the cloud to release computation burdenof the requester while preventing all
the above security andprivacy issues. With our scheme, workers’ data and identityprivacy are
well preserved. Meanwhile, the requester canverify the correctness of the result retrieved from
the cloud. Wealso extend our scheme to support data preprocessing, batchverification, and
efficient data update.Our Contributions: Generally speaking, we have made thefollowing major
contributions:\bullet We propose an efficient solution for the set operation inbig data analysis
based on the data collected from mobilecrowdsourcing. \bullet We introduce the cloud as an
intermediate entity to thetraditional mobile crowdsourcing, where worker’s dataprivacy and
identity privacy are well protected. \bullet For requesters, they can verify the correctness of
computationresults retrieved from the cloud.\bullet We further extend the basic scheme to useful
applicationsin big data analysis, such as data preprocessing, batchverification, and efficient data
update.
CONCLUSION
In this paper, we propose a scheme to enable the requesterto delegate set operations over
crowdsourced big data to thecloud. Meanwhile, worker’s data and identity privacy arepreserved,
and the requester can verify the correctness ofthe set operation result. We extend our scheme to
achievedata preprocessing, batch verification and data update are alsoproposed to reduce
computational costs of the system.
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CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249) MAIL ID: , [email protected]
Web: www.nexgenproject.com,
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