On practical privacy-preserving fault-tolerant data aggregation

Background The importance of privacy protection in analyses of personal data, such as genome-wide association studies (GWAS), has grown in recent years. GWAS focuses on identifying single-nucleotide polymorphisms (SNPs) associated with certain diseases such as cancer and diabetes, and the chi-squared (χ2) hypothesis test of independence can be utilized for this identification. However, recent studies have shown that publishing the results of χ2 tests of SNPs or personal data could lead to privacy violations. Several studies have proposed anonymization methods for χ2 testing with ε-differential privacy, which is the cryptographic community’s de facto privacy metric. However, existing methods can only be applied to 2×2 or 2×3 contingency tables, otherwise their accuracy is low for small numbers of samples. It is difficult to collect numerous high-sensitive samples in many cases such as COVID-19 analysis in its early propagation stage. Results We propose a novel anonymization method (RandChiDist), which anonymizes χ2 testing for small samples. We prove that RandChiDist satisfies differential privacy. We also experimentally evaluate its analysis using synthetic datasets and real two genomic datasets. RandChiDist achieved the least number of Type II errors among existing and baseline methods that can control the ratio of Type I errors. Conclusions We propose a new differentially private method, named RandChiDist, for anonymizing χ2 values for an I×J contingency table with a small number of samples. The experimental results show that RandChiDist outperforms existing methods for small numbers of samples.

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“…In recent years, -differential privacy [22] has been considered the de facto standard for privacy metrics [33,[41][42][43].…”

Section: Privacy Modelmentioning

confidence: 99%

“…(37) to the t value calculated using Eq. (43). When χ 2 * is greater than or equal to t, RandChiDist outputs "reject the null hypothesis H 0 , " and otherwise outputs "fail to reject the null hypothesis H 0 . "…”

Section: Differentially Private Hypothesis Testingmentioning

confidence: 99%

Privacy-preserving chi-squared test of independence for small samples

Sei

Ohsuga

2021

BioData Mining

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“…Shi et al [ 31 ] proposed a fault-tolerant protocol based on diverse groups. Grining et al [ 35 ] proposed a provable level of privacy even if massive devices malfunctioned. Nonetheless, the above traditional PPDA schemes did not adopt the architecture of edge-computing/fog-computing and suffered from latency problems.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT

Wang

2021

Sensors

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Edge computing has been introduced to the Internet of Things (IoT) to meet the requirements of IoT applications. At the same time, data aggregation is widely used in data processing to reduce the communication overhead and energy consumption in IoT. Most existing schemes aggregate the overall data without filtering. In addition, aggregation schemes also face huge challenges, such as the privacy of the individual IoT device’s data or the fault-tolerant and lightweight requirements of the schemes. In this paper, we present a privacy-preserving and lightweight selective aggregation scheme with fault tolerance (PLSA-FT) for edge computing-enhanced IoT. In PLSA-FT, selective aggregation can be achieved by constructing Boolean responses and numerical responses according to specific query conditions of the cloud center. Furthermore, we modified the basic Paillier homomorphic encryption to guarantee data privacy and support fault tolerance of IoT devices’ malfunctions. An online/offline signature mechanism is utilized to reduce computation costs. The system characteristic analyses prove that the PLSA-FT scheme achieves confidentiality, privacy preservation, source authentication, integrity verification, fault tolerance, and dynamic membership management. Moreover, performance evaluation results show that PLSA-FT is lightweight with low computation costs and communication overheads.

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“…In this section, an overview of existing fog-enabled privacy preserving-data aggregation (e.g., [9], [10], [12]- [14] and traditional privacy-preserving data aggregation (e.g., [17][18][19][20][21][22][23][24][25][26][27][28]) schemes in SG is provided in detail. Existing fog-enabled data aggregation schemes in SG, such as those presented in [9], [10], have a number of limitations.…”

Section: A Related Workmentioning

confidence: 99%

“…Similarly, the authors in [21], [22] have also proposed privacypreserving data aggregation schemes for ensuring integrity of metering data and securing SG communication respectively. Grining et al [23] presented a privacy-preserving data aggregation scheme, which is fault-tolerant for even against a massive number of malfunctioning nodes. Authors in [24], [25] have proposed fault-tolerant privacy-preserving data aggregation schemes which perform the aggregation of users' consumption data even if some of the SMs are malfunctioning.…”

Section: A Related Workmentioning

confidence: 99%

FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

Saleem

Khan

Malik

et al. 2020

IEEE Internet Things J.

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With advances in Fog and edge computing, various problems such as data processing for large Internet of things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for smart grids incur high computation and communication costs, and in recent years there have been efforts to leverage fog computing with smart grids to overcome these limitations. In this paper, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of Paillier crypto-system is used to encrypt consumption data of the smart meter users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the smart meters malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the PPFA aggregation scheme.

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On practical privacy-preserving fault-tolerant data aggregation

Cited by 9 publications

References 32 publications

Privacy-preserving chi-squared test of independence for small samples

Privacy-preserving chi-squared test of independence for small samples

Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT

FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

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