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Record W824459562

Practical Private Information Retrieval

2011· dissertation· en· W824459562 on OpenAlexfundno aff
Femi Olumofin

Bibliographic record

VenueUWSpace (University of Waterloo) · 2011
Typedissertation
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsnot available
FundersUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsThe InternetComputer sciencePrivate information retrievalInternet privacyWorld Wide WebInformation privacyPrivacy policyConfidentialityService providerInformation retrievalService (business)Computer securityBusiness
DOInot available

Abstract

fetched live from OpenAlex

In recent years, the subject of online privacy has been attracting much interest, especially as more Internet users than ever are beginning to care about the privacy of their online activities. Privacy concerns are even prompting legislators in some countries to demand from service providers a more privacy-friendly Internet experience for their citizens. These are welcomed developments and in stark contrast to the practice of Internet censorship and surveillance that legislators in some nations have been known to promote. The development of Internet systems that are able to protect user privacy requires private information retrieval (PIR) schemes that are practical, because no other efficient techniques exist for preserving the confidentiality of the retrieval requests and responses of a user from an Internet system holding unencrypted data. This thesis studies how PIR schemes can be made more relevant and practical for the development of systems that are protective of users' privacy.
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\nPrivate information retrieval schemes are cryptographic constructions for retrieving data from a database, without the database (or database administrator) being able to learn any information about the content of the query. PIR can be applied to preserve the confidentiality of queries to online data sources in many domains, such as online patents, real-time stock quotes, Internet domain names, location-based services, online behavioural profiling and advertising, search engines, and so on. 
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\nIn this thesis, we study private information retrieval and obtain results that seek to make PIR more relevant in practice than all previous treatments of the subject in the literature, which have been mostly theoretical. We also show that PIR is the most computationally efficient known technique for providing access privacy under realistic computation powers and network bandwidths. Our result covers all currently known varieties of PIR schemes. We provide a more detailed summary of our contributions below:
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\nOur first result addresses an existing question regarding the computational practicality of private information retrieval schemes. We show that, unlike previously argued, recent lattice-based computational PIR schemes and multi-server information-theoretic PIR schemes are much more computationally efficient than a trivial transfer of the entire PIR database from the server to the client (i.e., trivial download). Our result shows the end-to-end response times of these schemes are one to three orders of magnitude (10--1000 times) smaller than the trivial download of the database for realistic computation powers and network bandwidths. This result extends and clarifies the well-known result of Sion and Carbunar on the computational practicality of PIR.
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\nOur second result is a novel approach for preserving the privacy of sensitive constants in an SQL query, which improves substantially upon the earlier work. Specifically, we provide an expressive data access model of SQL atop of the existing rudimentary index- and keyword-based data access models of PIR. The expressive SQL-based model developed results in between 7 and 480 times improvement in query throughput than previous work. 
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\nWe then provide a PIR-based approach for preserving access privacy over large databases. Unlike previously published access privacy approaches, we explore new ideas about privacy-preserving constraint-based query transformations, offline data classification, and privacy-preserving queries to index structures much smaller than the databases. This work addresses an important open problem about how real systems can systematically apply existing PIR schemes for querying large databases.
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\nIn terms of applications, we apply PIR to solve user privacy problem in the domains of patent database query and location-based services, user and database privacy problems in the domain of the online sales of digital goods, and a scalability problem for the Tor anonymous communication network.
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\nWe develop practical tools for most of our techniques, which can be useful for adding PIR support to existing and new Internet system designs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.219
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

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