Privacy preserving veriﬁable set operation in big data for cloud-assisted mobile crowdsourcing

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

mailto:[email protected]



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




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.

REFERENCES




[1] S. S. Kanhere, “Participatory sensing: Crowdsourcing data from mobilesmartphones in urban

spaces,” in Mobile Data Management (MDM),2011 12th IEEE International Conference on, vol.

2. IEEE, 2011, pp.3–6.

[2] Q. Li and G. Cao, “Privacy-preserving participatory sensing.”

[3] ——, “Efficient and privacy-preserving data aggregation in mobilesensing,” in Network

Protocols (ICNP), 2012 20th IEEE InternationalConference on, Oct 2012, pp. 1–10.

[4] Q. Li, G. Cao, and T. La Porta, “Efficient and privacy-aware dataaggregation in mobile

sensing,” Dependable and Secure Computing,IEEE Transactions on, vol. 11, no. 2, pp. 115–129,

March 2014.

[5] C. Cornelius, A. Kapadia, D. Kotz, D. Peebles, M. Shin, and N. Triandopoulos,“Anonysense:

privacy-aware people-centric sensing,” in Proceedingsof the 6th international conference on

Mobile systems, applications,and services. ACM, 2008, pp. 211–224.

[6] R. Zhang, J. Shi, Y. Zhang, and C. Zhang, “Verifiable privacy-preservingaggregation in

people-centric urban sensing systems,” Selected Areasin Communications, IEEE Journal on, vol.

31, no. 9, pp. 268–278,September 2013.

[7] S. S. Kanhere, “Participatory sensing: Crowdsourcing data from mobilesmartphones in urban

spaces,” in Distributed computing and internettechnology. Springer, 2013, pp. 19–26.

[8] H. Yue, L. Guo, R. Li, H. Asaeda, and Y. Fang, “Dataclouds: Enablingcommunity-based

data-centric services over the internet of things,” IEEEInternet of Things Journal, vol. 1, no. 5,

pp. 472–482, Oct 2014.

[9] K. Hara, S. Azenkot, M. Campbell, C. L. Bennett, V. Le, S. Pannella,R. Moore, K. Minckler,

R. H. Ng, and J. E. Froehlich, “Improvingpublic transit accessibility for blind riders by




crowdsourcing bus stoplandmark locations with google street view: An extended analysis,”

ACMTransactions on Accessible Computing (TACCESS), vol. 6, no. 2, p. 5,2015.

[10] B. Liu, Y. Jiang, F. Sha, and R. Govindan, “Cloud-enabled privacypreservingcollaborative

learning for mobile sensing,” in Proceedingsof the 10th ACM Conference on Embedded

Network Sensor Systems.ACM, 2012, pp. 57–70.


Engineering

Privacy preserving veriﬁable set operation in big data for cloud-assisted mobile crowdsourcing