PrivRec: User-Centric Differentially Private Collaborative Filtering Using LSH and KD

Zhang, Yifei; Gao, Neng; Chen, Junsha; Wang, Jiong

doi:10.1007/978-3-030-63820-7_13

Cited by 2 publications

(6 citation statements)

References 10 publications

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“…Our work differs from [8,29,56] in the following points. First, [8,29,56] only analyzed LDP for hashes, and did not conduct a more challenging analysis of extended DP for inputs.…”

Section: Privacy-preserving Lshmentioning

confidence: 88%

“…Our work differs from [8,29,56] in the following points. First, [8,29,56] only analyzed LDP for hashes, and did not conduct a more challenging analysis of extended DP for inputs. In contrast, our work provides a careful analysis of extended DP, given that LSH preserves the original metric only approximately.…”

Section: Privacy-preserving Lshmentioning

confidence: 88%

“…Finally, we note that some studies have proposed privacy-preserving LSH [3,8,15,29,45,46,56]. However, some of them [3,15,46] only apply LSH and claim that it protects user privacy because LSH is a kind of non-invertible transformation.…”

Section: Privacy-preserving Lshmentioning

confidence: 95%

“…Specifically, they focused on a similarity search problem under the Jaccard similarity using up to 2000-dimensional vectors, and proposed an LDP algorithm based on MinHash. After the submission of our paper to a preprint [26], two related papers [29,56] have been published. Zhang et al [56] proposed an LDP algorithm for rating prediction based on MinHash and knowledge distillation.…”

Section: Privacy-preserving Lshmentioning

confidence: 99%

“…After the submission of our paper to a preprint [26], two related papers [29,56] have been published. Zhang et al [56] proposed an LDP algorithm for rating prediction based on MinHash and knowledge distillation. Hu et al [29] proposed an LDP algorithm based on LSH for federated recommender system.…”

Section: Privacy-preserving Lshmentioning

confidence: 99%

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Locality Sensitive Hashing with Extended Differential Privacy

Fernandes

Kawamoto

Murakami

2021

Lecture Notes in Computer Science

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Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while keeping high utility. However, existing works on extended DP are limited to few metrics, such as the Euclidean metric. Consequently, they have only a small number of applications, such as location-based services and document processing. In this paper, we propose a couple of mechanisms providing extended DP with a different metric: angular distance (or cosine distance). Our mechanisms are based on locality sensitive hashing (LSH), which can be applied to the angular distance and work well for personal data in a high-dimensional space. We theoretically analyze the privacy properties of our mechanisms, and prove extended DP for input data by taking into account that LSH preserves the original metric only approximately. We apply our mechanisms to friend matching based on high-dimensional personal data with angular distance in the local model, and evaluate our mechanisms using two real datasets. We show that LDP requires a very large privacy budget and that RAPPOR does not work in this application. Then we show that our mechanisms enable friend matching with high utility and rigorous privacy guarantees based on extended DP.

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“…Our work differs from [8,29,56] in the following points. First, [8,29,56] only analyzed LDP for hashes, and did not conduct a more challenging analysis of extended DP for inputs.…”

Section: Privacy-preserving Lshmentioning

confidence: 88%

Section: Privacy-preserving Lshmentioning

confidence: 88%

Section: Privacy-preserving Lshmentioning

confidence: 95%

Section: Privacy-preserving Lshmentioning

confidence: 99%

Section: Privacy-preserving Lshmentioning

confidence: 99%

See 3 more Smart Citations

Locality Sensitive Hashing with Extended Differential Privacy

Fernandes

Kawamoto

Murakami

2021

Lecture Notes in Computer Science

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Locality-Sensitive Hashing Does Not Guarantee Privacy! Attacks on Google's FLoC and the MinHash Hierarchy System

Florian

Kubíček

Cotrini

et al. 2023

PoPETs

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Recently proposed systems aim at achieving privacy using locality-sensitive hashing. We show how these approaches fail by presenting attacks against two such systems: Google's FLoC proposal for privacy-preserving targeted advertising and the MinHash Hierarchy, a system for processing location trajectories in a privacy-preserving way. Our attacks refute the pre-image resistance, anonymity, and privacy guarantees claimed for these systems. In the case of FLoC, we show how to deanonymize users using Sybil attacks and to reconstruct 10% or more of the browsing history for 30% of its users using Generative Adversarial Networks. We achieve this only analyzing the hashes used by FLoC. For MinHash, we precisely identify the location trajectory of a subset of individuals and, on average, we can limit users' trajectory to just 10% of the possible geographic area, again using just the hashes. In addition, we refute their differential privacy claims.

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PrivRec: User-Centric Differentially Private Collaborative Filtering Using LSH and KD

Cited by 2 publications

References 10 publications

Locality Sensitive Hashing with Extended Differential Privacy

Locality Sensitive Hashing with Extended Differential Privacy

Locality-Sensitive Hashing Does Not Guarantee Privacy! Attacks on Google's FLoC and the MinHash Hierarchy System

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