Querying for Queries

Hafiz, Syed Mahbub; Henry, Ryan

doi:10.1145/3133956.3134008

Cited by 8 publications

(13 citation statements)

References 14 publications

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“…By default, Hafiz-Henry assumes users fetch records via positional queries [23]; that is, via their row numbers within D. We remark that (i) the DPFs naturally support keyword-based queries [12] as a more user-friendly alternative and (ii) this extension "plays well" with Hafiz-Henry queries. Beyond the preceding remark, we do not concern ourselves with how users specify which records they seek (though we do consider a candidate user interface in Section 9); however, we do prescribe one minor change to Hafiz-Henry query generation in support of PIRSONA functionality: The DeMUX assumes a so-called leafless seed for the "least-significant-bit" DPF (see §6.3) and, consequently, that the servers substitute the bit vector flags([[(j,…”

Section: Query Representationmentioning

confidence: 98%

You May Also Like... Privacy: Recommendation Systems Meet PIR

Vadapalli

Bayatbabolghani

Henry

2021

Proceedings on Privacy Enhancing Technologies

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“…The user then performs a component-wise secret reconstruction using the responses received from the servers to obtain the desired block of data. Following in the footsteps of [7], we select Shamir's secret sharing scheme [17] as proposed in Goldberg's IT-PIR protocol [28] as our choice of linear secret sharing scheme. Shamir's (t + 1, ℓ)-threshold scheme in the vector-matrix model ensures that the user obtains their desired results as long as t + 1 or more servers out of ℓ respond.…”

Section: A Vector-matrix Pir Modelmentioning

confidence: 99%

“…Our protocol exploits the concept of indexes of queries introduced in Hafiz-Henry's protocol. The protocol introduced in [7] utilizes indexes of queries to support expressive queries that utilize contextual information rather than positional information to fetch relevant data blocks. Although Chor et al [30] introduced a scheme for keyword-based querying that translates keyword searches into positional queries, is comparable but with multiple rounds of interactions, [7]'s index of queries mechanism was more suitable for our protocol since it requires a single round of interaction to fetch the data, thus minimizing communication costs.…”

Section: A Vector-matrix Pir Modelmentioning

confidence: 99%

“…A data structure called a permutation matrix is at the heart of the idea of indexes of queries, as introduced in [7]. Permutation matrices are constructed by permuting the rows of an identity matrix.…”

Section: Index Of Queries Techniquementioning

confidence: 99%

“…A primary reason for this is that most protocols cannot support commonly used aggregate queries with a provable privacy guarantee. We develop a novel framework for information-theoretic private information retrieval protocols -that utilizes the concept of introducing sparse matrix-like auxiliary data structures similar to Hafiz-Henry's Querying for Queries work [7] to support various aggregate queries frequently used in data analytics workflows. Unlike the vast majority of existing protocols, our scheme does not require physical positional information to fetch aggregated results from a database, and it just requires contextual information about the data to fetch the required blocks of data.…”

Section: Introductionmentioning

confidence: 99%

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Private Aggregate Queries to Untrusted Databases

Hafiz,

Gupta,

Wnuck

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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An essential part of ensuring privacy for internet service users is to protect what data they access so that the database host cannot infer sensitive information (e.g., political affiliation, sexual orientation, etc.) from the query pattern to exploit it or share it with third parties. Often, database users submit aggregate queries (e.g., SUM, MEAN, etc.) with searching and filtering constraints to extract statistically meaningful information from a database by seeking the privacy of its query's sensitive values and database interactions. Private information retrieval (PIR), a privacy-preserving cryptographic tool, solves a simplified version of this problem by hiding the database item that a client accesses. Most PIR protocols require the client to know the exact row index of the intended database item, which cannot support the complicated aggregation-based statistical query in a similar setting. Some works in the PIR space contain keyword searching and SQL-like queries, but most need multiple interactions between the PIR client and PIR servers. Some schemes support searching SQL-like expressive queries in a single round but fail to enable aggregate queries. These schemes are the main focus of this paper. To bridge the gap, we have built a general-purpose novel information-theoretic PIR (IT-PIR) framework that permits a user to fetch the aggregated result, hiding all sensitive sections of the complex query from the hosting PIR server in a single round of interaction. In other words, the server will not know which records contribute to the aggregation. We then evaluate the feasibility of our protocol for both benchmarking and real-world application settings. For instance, in a complex aggregate query to the Twitter microblogging database of 1 million tweets, our protocol takes 0.014 seconds for a PIR server to generate the result when the user is interested in one of ∼ 3𝑘 user handles. In contrast, for a much-simplified task, not an aggregate but a positional query, Goldberg's regular IT-PIR (Oakland 2007) takes 1.13 seconds. For all possible user handles, 300𝑘, it takes equal time compared to the regular IT-PIR. This example shows that complicated aggregate queries through our framework do not incur additional overhead if not less, compared to the conventional query.

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Puncturable Pseudorandom Sets and Private Information Retrieval with Near-Optimal Online Bandwidth and Time

Shi¹,

Aqeel²,

Chandrasekaran

et al. 2021

Lecture Notes in Computer Science

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Querying for Queries

Cited by 8 publications

References 14 publications

You May Also Like... Privacy: Recommendation Systems Meet PIR

You May Also Like... Privacy: Recommendation Systems Meet PIR

Private Aggregate Queries to Untrusted Databases

Puncturable Pseudorandom Sets and Private Information Retrieval with Near-Optimal Online Bandwidth and Time

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