&lt;inline-formula&gt; 
                     &lt;tex-math notation="LaTeX"&gt;$\textsf{LoPub}$ &lt;/tex-math&gt;
                  &lt;/inline-formula&gt;: High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

Ren, Xuebin; Yu, Chia-Fu; Yu, Weiren; Yang, Shusen; Yang, Xinyu; McCann, Julie A.; Yu, Philip S.

doi:10.1109/tifs.2018.2812146

Cited by 216 publications

(32 citation statements)

References 39 publications

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“…After that, the LDP technique has been widely applied in the industry to protect their users' privacy, like iOS for Apple [60] Win 10 for Microsoft [14], and Samsung [47]. Since the concept of LDP was proposed, it has been widely applied in multiple fields, including multi-attribute values estimation [18], [53] marginal release [13], [73], time series data release [69], graph data collection [59], [67], [68], key-value data collection [28], [70], [71] and private learning [74], [75].…”

Section: Related Workmentioning

confidence: 99%

Differential Aggregation against General Colluding Attackers

Du¹,

Ye²,

Fu³

et al. 2023

Preprint

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Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and analyze sensitive data while preserving users' privacy. However, almost all LDP protocols rely on a semi-trust model where users are curiousbut-honest, which rarely holds in real-world scenarios. Recent works [7], [12], [65] show poor estimation accuracy of many LDP protocols under malicious threat models. Although a few works have proposed some countermeasures to address these attacks, they all require prior knowledge of either the attacking pattern or the poison value distribution, which is impractical as they can be easily evaded by the attackers.In this paper, we adopt a general opportunistic-and-colluding threat model and propose a multi-group Differential Aggregation Protocol (DAP) to improve the accuracy of mean estimation under LDP. Different from all existing works that detect poison values on individual basis, DAP mitigates the overall impact of poison values on the estimated mean. It relies on a new probing mechanism EMF (i.e., Expectation-Maximization Filter) to estimate features of the attackers. In addition to EMF, DAP also consists of two EMF postprocessing procedures (EMF* and CEMF*), and a group-wise mean aggregation scheme to optimize the final estimated mean to achieve the smallest variance. Extensive experimental results on both synthetic and real-world datasets demonstrate the superior performance of DAP over state-of-the-art solutions.

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Section: Related Workmentioning

confidence: 99%

Differential Aggregation against General Colluding Attackers

Du¹,

Ye²,

Fu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…(ε -Local Differential Privacy, ε -LDP [7]) A randomized algorithm f :𝕋 → 𝕐 with domain 𝕋 and range 𝕐 satisfies ϵ-LDP if and only if, for any inputs t, t ′ ∈ 𝕋 and output y ∈ 𝕐:…”

Section: Local Differential Privacymentioning

confidence: 99%

Communication Efficient Federated Personalized Recommendation

Wei¹

2023

FCIS

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Recommendation systems that can correctly predict user preferences in the information age have become an important factor for business success. However, recommendation systems require users' personal information, and centralized collection and processing of user data may lead to serious privacy risks. Good progress has been made in recent years using federated learning techniques for privacy-preserving recommendations, but several key challenges remain to be addressed: most federated recommender systems ignore communication process optimization, inequities in aggregation of federated models, and lack of personalization to users. In this paper, we propose a communication efficient and fair personalized federated recommendation approach (CFFR) to address these challenges. CFFR uses adaptive client group selection to personalize models while accelerating the training process. A fair-aware model aggregation algorithm is proposed that adaptively captures the performance and data imbalance among different clients to address the unfairness problem. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method.

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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%

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

Sei

Ohsuga

2021

BioData Mining

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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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<inline-formula> <tex-math notation="LaTeX">$\textsf{LoPub}$ </tex-math> </inline-formula>: High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

Cited by 216 publications

References 39 publications

Differential Aggregation against General Colluding Attackers

Differential Aggregation against General Colluding Attackers

Communication Efficient Federated Personalized Recommendation

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

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