Computing Local Sensitivities of Counting Queries with Joins

Tao, Yuchao; He, Xi; Machanavajjhala, Ashwin; Roy, Sudeepa

doi:10.1145/3318464.3389762

Cited by 30 publications

(16 citation statements)

References 37 publications

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“…In this paper, we start by studying how to choose a near-optimal 𝜏 in a DP manner in the presence of self-joins. As with all prior 𝜏selection mechanisms over mean (sum) estimation [2,3,19,28,34] and self-join-free queries [38], we assume that the global sensitivity of the given query 𝑄 is bounded by 𝐺𝑆 𝑄 . Since one tends to set a large 𝐺𝑆 𝑄 as argued in Example 1.1, we must try to minimize the dependency on 𝐺𝑆 𝑄 .…”

Section: Our Contributionsmentioning

confidence: 99%

R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

Dong

Fang

et al. 2022

Proceedings of the 2022 International Conference on Management of Data

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Section: Our Contributionsmentioning

confidence: 99%

R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

Dong

Fang

et al. 2022

Proceedings of the 2022 International Conference on Management of Data

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“…Bounding privacy loss. There is a series of work investigating approaches to constrain the privacy loss of queries or transformations with unbounded stability [44,54,55,62,80,85]. However these works are conducted under the scope of standard databases rather than secure outsourced databases.…”

Section: Related Workmentioning

confidence: 99%

“…However these works are conducted under the scope of standard databases rather than secure outsourced databases. Moreover, most of the works consider to bound the privacy loss of a single query or one-time transformation [44,62,80,85]. In this work, we consider constraining the privacy loss of a composed transformation, which may contain an infinite number of sub-transformations.…”

Section: Related Workmentioning

confidence: 99%

IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy

Wang¹,

Bater²,

Nayak³

et al. 2022

Preprint

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In this paper, we consider secure outsourced growing databases that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view, such that they can use only the materialized view to process query requests instead of accessing the original data from which the view was derived. To tackle this, we devise a novel view-based secure outsourced growing database framework, IncShrink. The key features of this solution are: (i) IncShrink maintains the view using incremental MPC operators which eliminates the need for a trusted third party upfront, and (ii) to ensure high performance, IncShrink guarantees that the leakage satisfies DP in the presence of updates. To the best of our knowledge, there are no existing systems that have these properties. We demonstrate IncShrink's practical feasibility in terms of efficiency and accuracy with extensive empirical evaluations on real-world datasets and the TPC-ds benchmark. The evaluation results show that IncShrink provides a 3-way trade-off in terms of privacy, accuracy and efficiency guarantees, and offers at least a 7,800× performance advantage over standard secure outsourced databases that do not support view-based query paradigm.

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“…For releasing linear statistics over relational databases using SQL, local sensitivity has been used for answering full acyclic join queries [40]. This approach lacks generality since we cannot compute some functions as EBC without cyclic joins and GROUPBY clauses.…”

Section: Related Workmentioning

confidence: 99%

Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity

Farias¹,

Brito²,

Flynn³

et al. 2020

Preprint

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Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the noisy output of numeric queries (e.g., using the Laplace mechanism), based on the notions of global sensitivity and local sensitivity. However, although there has been some work on generic mechanisms for releasing the output of non-numeric queries using global sensitivity (e.g., the Exponential mechanism), the literature lacks generic mechanisms for releasing the output of non-numeric queries using local sensitivity to reduce the noise in the query output.In this work, we remedy this shortcoming and present the local dampening mechanism. We adapt the notion of local sensitivity for the non-numeric setting and leverage it to design a generic nonnumeric mechanism. We illustrate the effectiveness of the local dampening mechanism by applying it to two diverse problems: (i) Influential node analysis. Given an influence metric, we release the top-k most influential nodes while preserving the privacy of the relationship between nodes in the network; (ii) Decision tree induction. We provide a private adaptation to the ID3 algorithm to build decision trees from a given tabular dataset. Experimental results show that we could reduce the use of privacy budget by 3 to 4 orders of magnitude for Influential node analysis and increase accuracy up to 12% for Decision tree induction when compared to global sensitivity based approaches.

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Computing Local Sensitivities of Counting Queries with Joins

Cited by 30 publications

References 37 publications

R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys

IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy

Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity

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