Differentially Private Two-Party Set Operations

Kacsmar, Bailey; Khurram, Basit; Lukas, Nils; Norton, Alexander; Shafieinejad, Masoumeh; Shang, Zhiwei; Baseri, Yaser; Sepehri, Maryam; Oya, Simon; Kerschbaum, Florian

doi:10.1109/eurosp48549.2020.00032

Cited by 12 publications

(20 citation statements)

References 31 publications

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“…The second part is security analysis, where we follow the standard simulation-based paradigm to prove data confidentiality against the cloud servers. Similar treatment also appears in prior works [38], [57] using both DP and cryptography.…”

Section: Privacy and Security Analysismentioning

confidence: 52%

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Wang¹,

Zheng²,

Jia³

et al. 2022

Preprint

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Analytics over social graphs allows to extract valuable knowledge and insights for many fields like community detection, fraud detection, and interest mining. In practice, decentralized social graphs frequently arise, where the social graph is not available to a single entity and is decentralized among a large number of users, each holding only a limited local view about the whole graph. Collecting the local views for analytics of decentralized social graphs raises critical privacy concerns, as they encode private information about the social interactions among individuals. In this paper, we design, implement, and evaluate PrivGED, a new system aimed at privacy-preserving analytics over decentralized social graphs. PrivGED focuses on the support for eigendecomposition, one popular and fundamental graph analytics task producing eigenvalues/eigenvectors over the adjacency matrix of a social graph and benefits various practical applications. PrivGED is built from a delicate synergy of insights on graph analytics, lightweight cryptography, and differential privacy, allowing users to securely contribute their local views on a decentralized social graph for a cloud-based eigendecomposition analytics service while gaining strong privacy protection. Extensive experiments over real-world social graph datasets demonstrate that PrivGED achieves accuracy comparable to the plaintext domain, with practically affordable performance superior to prior art.

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Section: Privacy and Security Analysismentioning

confidence: 52%

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Wang¹,

Zheng²,

Jia³

et al. 2022

Preprint

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show abstract

“…Additionally, other libraries and platforms exist, which, while they did not comply with our inclusion criteria or were recently released, propose DP functionalities that are worth clustering and benchmarking. We have found the following tools: Google's Rappor [61], DJoin [62], ARX [63], PSI [64], Arivat [65], a DP violation detector [66], and Google's ZetaSQL [67]. We do not recommend benchmarking PINQ [16] or GUPT [17] as they are deprecated.…”

Section: Discussionmentioning

confidence: 99%

Do I Get the Privacy I Need? Benchmarking Utility in Differential Privacy Libraries

Garrido¹,

Near²,

Aitsam³

et al. 2021

Preprint

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An increasing number of open-source libraries promise to bring differential privacy to practice, even for non-experts. This paper studies five libraries that offer differentially private analytics: Google DP, SmartNoise, diffprivlib, diffpriv, and Chorus. We compare these libraries qualitatively (capabilities, features, and maturity) and quantitatively (utility and scalability) across four analytics queries (count, sum, mean, and variance) executed on synthetic and realworld datasets. We conclude that these libraries provide similar utility (except in some notable scenarios). However, there are significant differences in the features provided, and we find that no single library excels in all areas. Based on our results, we provide guidance for practitioners to help in choosing a suitable library, guidance for library designers to enhance their software, and guidance for researchers on open challenges in differential privacy tools for non-experts.

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“…Kacsmar et al combine differentially private mechanisms with fully homomorphic encryption schemes to tackle the problem of private set intersection. 27 In particular, they map elements to bins within a table by hashing-to-bins techniques. [94][95][96] Then, these plaintext is combined into a vector by a batching technique, and the vector is encrypted by fully homomorphic encryption scheme (BGV 97 ).…”

Section: Applicationsmentioning

confidence: 99%

“…Some technologies related to cryptography are promising to improve data utility of differentially private mechanisms by recent researches such as anonymous communication technology [23][24][25] and homomorphic encryption schemes. [26][27][28] Therefore, we are motivated to provide this review to summarize how to improve data utility of differential privacy and to benefit future researches about improving data utility of differential privacy by cryptography.…”

mentioning

confidence: 99%

Differential privacy: Review of improving utility through cryptography‐based technologies

Huang

Zhuo

Zhu

et al. 2022

Concurrency and Computation

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Due to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information.Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention.In this review, we focus on improving data utility of differentially private mechanisms through technologies related to cryptography. In particular, we first focus on how to improve data utility through anonymous communication. Then, we summarize how to improve data utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' data utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state-of-the-art and to benefit future researches, we are motivated to provide this review.

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Differentially Private Two-Party Set Operations

Cited by 12 publications

References 31 publications

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Do I Get the Privacy I Need? Benchmarking Utility in Differential Privacy Libraries

Differential privacy: Review of improving utility through cryptography‐based technologies

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