Proactive fault-tolerant aggregation protocol for privacy-assured smart metering

Won, Jongok; Chris, Y. T.; Yau, David K. Y.; Rao, Nageswara S. V.

doi:10.1109/infocom.2014.6848230

Cited by 39 publications

(79 citation statements)

References 10 publications

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“…Here, we assume that the final result SUM(x(t)) is positive and bounded from above by a large prime number . We assume that participants have a bidirectional communication channel with the aggregator like [17]. The participants are not connected to each other directly, but they can exchange encrypted messages among themselves via the aggregator or intermediate routers.…”

Section: Problem Definition and Threat Modelmentioning

confidence: 99%

Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol

Liu

Wang

et al. 2016

International Journal of Distributed Sensor Networks

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Many miraculous ideas have been proposed to deal with the privacy-preserving time-series data aggregation problem in pervasive computing applications, such as mobile cloud computing. The main challenge consists in computing the global statistics of individual inputs that are protected by some confidentiality mechanism. However, those works either suffer from collusive attack or require time-consuming initialization at every aggregation request. In this paper, we proposed an efficient aggregation protocol which tolerates up to passive adversaries that do not try to tamper the computation. The proposed protocol does not require a trusted key dealer and needs only one initialization during the whole time-series data aggregation. We formally analyzed the security of our protocol and results showed that the protocol is secure if the Computational Diffie-Hellman (CDH) problem is intractable. Furthermore, the implementation showed that the proposed protocol can be efficient for the time-series data aggregation.

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Section: Problem Definition and Threat Modelmentioning

confidence: 99%

Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol

Liu

Wang

et al. 2016

International Journal of Distributed Sensor Networks

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“…A recent scheme [13] introduced a design of future ciphertext, a buffering mechanism, to deal with fault tolerance, which can efficiently achieve low aggregation error. But when coping with dynamic joins and leaves, the scheme still has high communication overhead Oðk Â BÞ (see Table 1 for parameter explanation).…”

Section: Related Workmentioning

confidence: 99%

“…In addition, none of these schemes [8][9][10][11][12][13] considers the data integrity, which is especially important to the encryption schemes in [10,12,13] based on the modular addition operation.…”

Section: Related Workmentioning

confidence: 99%

“…So if a single participant does not respond, it will lead to that the aggregator can neither obtain the aggregate result from the received reports nor verify aggregation integrity. We will deal with the problem by leveraging future message buffering mechanism similar to the future ciphertext buffering mechanism [13].…”

Section: Dealing With Fault Tolerancementioning

confidence: 99%

“…We measure the communication and computation overhead of our scheme when dynamic joins and leaves happen, and also the computation overhead of the mobile nodes (i.e., participants) and the aggregator to encrypt and decrypt the sensing data, separately. We compare our scheme against three other schemes in [9,12,13] denoted by SCRCS, LC and WMYR, respectively.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Private data aggregation with integrity assurance and fault tolerance for mobile crowd-sensing

Chen

Zhang

2015

Wireless Netw

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Mobile crowd-sensing can learn the aggregate statistics over personal data to produce useful knowledge about the world. Since personal data may be privacy-sensitive, the aggregator should only gain desired statistics without learning anything about the personal data. To guarantee differential privacy of personal data under an untrusted aggregator, existing approaches encrypt the noisy personal data, and allow the aggregator to get a noisy sum. However, these approaches lack of either efficient support of dynamic joins and leaves, or secure data-integrity verification, or fault tolerance. In this paper, we propose a novel private data aggregation scheme to address these issues for mobile crowdsensing applications. In our scheme, we first design an efficient group management protocol to deal with the participants' dynamic joins and leaves. Then we enhance the scheme with data-integrity verification by considering the security vulnerability of limited data range. Moreover, we guarantee fault tolerance by leveraging a future message buffering mechanism, enabling continuously obtaining aggregate results and integrity verifications when failures happen. The analysis indicates that our scheme achieves desired properties, and the performance evaluation demonstrates the scheme's efficiency in terms of communication and computation overhead.

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A lightweight privacy‐preserving scheme with data integrity for smart grid communications

Bao

2015

Concurrency and Computation

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Smart grid, deemed as the next generation of power grid, can efficiently monitor, control, and predicate energy generation and consumption. However, the frequent collection of users' consumption information in smart grid may reveal user's privacy, and the tampering of smart grid communication may also impair the data integrity, subsequently affecting the precise monitoring and controlling at the control center. In this paper, to address the aforementioned challenges, we propose a lightweight data report scheme for smart grid communications, which can achieve privacy preservation and data integrity simultaneously. Specifically, an efficient pseudonym identity-based privacy-preserving report approach is proposed for the control center to obtain the fine-grained usage data of all the users while protecting user's privacy. An online/off-line hash tree-based mechanism is also designed to check and assure data integrity of communications. Because of the shifting of most time-consuming computations to off-line phase, the online process is very fast and efficient by performing merely the lightweight bottom-up hash tree verifications to check all users' data integrity concurrently. Furthermore, a topology-independent data report architecture is also structured, which is adaptable for dynamic residential users to spontaneously form clusters and efficiently report data in flocks. Extensive performance evaluation demonstrates that the proposed scheme can achieve less communication overhead and dramatically reduce computational cost in comparison with the existing schemes. Copyright into two phases. One is off-line phase (during the report intervals) and the other is online phase (at report time point), which uses the pre-stored results in off-line phase to report usage data efficiently. Specifically, the main contributions of this paper are threefold.Firstly, a lightweight pseudonym identity-based privacy-preserving data report approach is proposed. Different from the existing data aggregation schemes, in which just the sum usage data can be obtained by the control center (CC), the fine-grained usage data of all the users can be obtained by CC in privacy-preserving way. Thus, provided that user's privacy is not revealed, with the detailed information, the whole system can be monitored and controlled more efficiently by CC. Secondly, an online/off-line hash tree-based authentication and data integrity verification mechanism is designed. Most of the computations of the smart meter with limited resources could be pre-processed in off-line phase. Furthermore, source authentication and data integrity of all the received usage reports can be checked simultaneously by performing the bottom-up hash tree verification procedures. Finally, a distributed and autonomous data collection architecture is structured. The users in the neighboring areas can form the cluster dynamically and flexibly, which makes the data report to be topology independent. And extensive performance evaluation demonstrates that the proposed architecture can achie...

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Proactive fault-tolerant aggregation protocol for privacy-assured smart metering

Cited by 39 publications

References 10 publications

Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol

Collusion-Tolerable and Efficient Privacy-Preserving Time-Series Data Aggregation Protocol

Private data aggregation with integrity assurance and fault tolerance for mobile crowd-sensing

A lightweight privacy‐preserving scheme with data integrity for smart grid communications

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