Publicly Verifiable Computation of Polynomials Over Outsourced Data With Multiple Sources

Song, Wei; Wang, Bing; Wang, Qian; Shi, Chengliang; Lou, Wenjing; Peng, Zhiyong

doi:10.1109/tifs.2017.2705628

Cited by 35 publications

(25 citation statements)

References 21 publications

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“…Arithmetic circuit [2,27] is a basic architecture that can be exploited to compute arbitrary polynomials. In our V-PATD, the arithmetic circuit is an important aid to verify the integrity of the results returned by the server.…”

Section: Arithmetic Circuitmentioning

confidence: 99%

“…(3), we know that the server needs to perform polynomial and logarithmic operations during the truth discovery process. However, it usually involves expensive cost to generate proof s for exponential results [2,27]. In our V-PATD, we only require the server to compute the polynomials in Eqn.…”

Section: Verification Mechanismmentioning

confidence: 99%

“…To the best of our knowledge, most existing verifiable computation schemes [9,14,24,25,34] only support verification with a single data source or fixed outsourced functions, and hard to keep a small overhead when dealing with large-scale data and users. Recently, Song et al [27] proposed the first publicly verifiable computation of polynomials framework over outsourced data. The authors claimed that their verifiable approach satisfied the above characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy" cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads.

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Section: Arithmetic Circuitmentioning

confidence: 99%

Section: Verification Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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“…Hence, clients need some efficient methodologies to guarantee that the returned result from servers is correct, which is a necessity in the outsourced computation paradigm.The design of verifiable outsourced computation has drawn widespread attentions. 2,[6][7][8][9] In the verifiable computation scheme, the verifiability property allows the clients to verify the integrity of the result. If the result is performed properly, the verifiers can accept the result and continue with the follow-up work.…”

mentioning

confidence: 99%

“…scenario was introduced by Choi et al, 12 in which several resource-constrained clients can perform the desired function f on their collective input (x 1 , … , x n ). Additionally, several subsequent works such as other works 6,7,13 focus on the multi-client scenario as well.…”

mentioning

confidence: 99%

An improved scheme for outsourced computation with attribute‐based encryption

Yang

Sun

Qin

et al. 2018

Concurrency and Computation

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With the wide deployment of cloud computing, outsourcing complicated computational tasks to cloud service providers has attracted much attention. An increasing number of clients with computationally constrained devices choose to outsource their heavy tasks to cloud servers to reduce the computational overhead in local. However, how to preserve the integrity of computational results becomes a challenge since commercial cloud servers are not trusted. Public verifiability is an effective mechanism to allow clients to verify the integrity of the results returned by the servers. Because the results are sensitive in many applications, it raises the problem of privacy leakage in the public verification process. In this paper, we propose an efficient verifiable computation scheme while keeping output privacy. The proposed scheme achieves blind verifiability such that the verifiers who have not the additional information (retrieve key) can verify the integrity of the result without learning the result. Furthermore, by combining with (k, n)-threshold sharing, our scheme allows the clients jointly learn the results. KEYWORDSblind verifiability, cloud computing, KP-ABE, verifiable computation INTRODUCTIONWith the rapid development of cloud technology, outsourced computation has been an important field. The technology provides not only the storage service but also the computing service, which can benefit the clients with limited resources a great deal. Meanwhile, the computationally constrained devices such as smartphones, laptops, and wearable equipments 1 have become popular. These scenarios contribute to the research of outsourced computation where the computationally weak clients (devices) outsource the expensive and complicated computation to the large and powerful servers. 2 In addition, outsourced computation is sometimes used to perform the complicated task in some cryptographic primitives 3-5 as well, which can make the primitives more efficient and practical. However, the servers are generally operated by the third-party outside the control domain of clients, and there may exist an incentive for servers to deceive clients into accepting an incorrect result. For instance, to save the computational resources, the cloud servers may not perform the advertised computation but instead return a computationally indistinguishable result to the client.In this case, the key problem is that the clients may not have enough computational resources to detect the incorrect result, which would bring trouble to clients in their subsequent works. For example, if the computational result is the statistics analysis for stock market, the incorrect result will cause the investment corporation property loss. Hence, clients need some efficient methodologies to guarantee that the returned result from servers is correct, which is a necessity in the outsourced computation paradigm.The design of verifiable outsourced computation has drawn widespread attentions. 2,6-9 In the verifiable computation scheme, the verifiability property allows the cl...

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Select the Best for Me: Privacy-Preserving Polynomial Evaluation Algorithm over Road Network

Song

Shi

Shen

et al. 2019

Database Systems for Advanced Applications

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Publicly Verifiable Computation of Polynomials Over Outsourced Data With Multiple Sources

Cited by 35 publications

References 21 publications

Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

An improved scheme for outsourced computation with attribute‐based encryption

Select the Best for Me: Privacy-Preserving Polynomial Evaluation Algorithm over Road Network

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