”Better Than Nothing” Privacy with Bloom Filters: To What Extent?

Bianchi, Giuseppe; Bracciale, Lorenzo; Loreti, Paola

doi:10.1007/978-3-642-33627-0_27

Cited by 46 publications

(71 citation statements)

References 25 publications

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“…To implement the binary encoding of phone numbers (see Algorithm 2 line 7), we use a Reed Muller error-correcting code, RM (3,5); this is a [32,26,4] 2 code with relative distance equal to 1/8 and error correcting capability equal to 1 bit [17]. Notice that k = 26 bits is sufficient to encode 7-digit phone numbers, which requires a minimum of 24 bits.…”

Section: Ldp Protocol Configurationmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Practical Differentially Private Collaborative Phone Blacklisting System

Ucci

Perdisci

Lee

et al. 2020

Annual Computer Security Applications Conference

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Section: Ldp Protocol Configurationmentioning

confidence: 99%

“…To correctly reconstruct the value v, at least one ball must fill each bin. As reported in [5], the number of non-empty bins resulting from randomly inserting n balls into l bins has the following probability distribution:…”

Section: Analysis Of Area Code Bucketizationmentioning

confidence: 99%

Towards a Practical Differentially Private Collaborative Phone Blacklisting System

Ucci

Perdisci

Lee

et al. 2020

Annual Computer Security Applications Conference

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“…A Bloom filter hashes and blends elements into one array of bits. When used in the context of privacy, it was demonstrated that a server using Bloom filters cannot infer any information on one specific entry [2]. However, a Bloom filter can not be used as is in our application, but it needs two adaptations.…”

Section: Sketching Techniquesmentioning

confidence: 99%

Privacy Preserving Group Membership Verification and Identification

Gheisari

Furon

Amsaleg

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Similarly, group membership identification states which group the individual belongs to, without knowing his/her identity. A recent contribution provides privacy and security for group membership protocols through the joint use of two mechanisms: quantizing biometric templates into discrete embeddings, and aggregating several templates into one group representation.This paper significantly improves that contribution because it jointly learns how to embed and aggregate instead of imposing fixed and hard coded rules. This is demonstrated by exposing the mathematical underpinnings of the learning stage before showing the improvements through an extensive series of experiments targeting face recognition.Overall, experiments show that learning yields an excellent trade-off between security / privacy and the verification / identification performances.

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“…The privacy guarantees provided by plain Bloom filters has been formally studied by Bianchi, Bracciale, and Loreti [10]. Their study highlights their inherent limitation, when the universe can be enumerated, which is the case with network identifiers like MAC addresses.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving Wi-Fi Analytics

Alaggan

Cunche

Gambs

2018

Proceedings on Privacy Enhancing Technologies

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As communications-enabled devices are becoming more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcasted by their devices. Thus, while there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while satisfying the strong guarantee of ε-differential privacy. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters called Pan-Private BLIP. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection and more generally t-out-of-n cardinalities). Finally, we evaluate expirementally our approach and compare it to previous ones on real datasets.

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”Better Than Nothing” Privacy with Bloom Filters: To What Extent?

Cited by 46 publications

References 25 publications

Towards a Practical Differentially Private Collaborative Phone Blacklisting System

Towards a Practical Differentially Private Collaborative Phone Blacklisting System

Privacy Preserving Group Membership Verification and Identification

Privacy-preserving Wi-Fi Analytics

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