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Private information retrieval

Private information retrieval is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Private information retrieval rather than just read about it. In short: In cryptography, a private information retrieval (PIR) protocol is a protocol that allows a user to retrieve an item from a server in possession of a database without revealing which item is retrieved. PIR is a weaker version of 1-out-of-n oblivious transfer, where it is also required that the user should not get information about other database items.

Key takeaways

  • Private information retrieval belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Private information retrieval to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Private information retrieval from memory before moving on to harder problems.

Reference excerpt

In cryptography, a private information retrieval (PIR) protocol is a protocol that allows a user to retrieve an item from a server in possession of a database without revealing which item is retrieved. PIR is a weaker version of 1-out-of-n oblivious transfer, where it is also required that the user should not get information about other database items. One trivial, but very inefficient way to achieve PIR is for the server to send an entire copy of the database to the user. In fact, this is the only possible protocol (in the classical or the quantum setting) that gives the user information-theoretic privacy for their query in a single-server setting. There are two ways to address this problem: make the server computationally bounded or assume that there are multiple non-cooperating servers, each having a copy of the database. The problem was introduced in 1995 by Chor, Goldreich, Kushilevitz and Sudan in the information-theoretic setting and in 1997 by Kushilevitz and Ostrovsky in the computational setting. Since then, very efficient solutions have been discovered. Single database (computationally private) PIR can be achieved with constant (amortized) communication and k-database (information theoretic) PIR can be done with n O ( log ⁡ log ⁡ k k log ⁡ k ) {\displaystyle n^{O\left({\frac {\log \log k}{k\log k}}\right)}} communication.

Advances in computational PIR The first single-database computational PIR scheme to achieve communication complexity less than n {\displaystyle n} was created in 1997 by Kushilevitz and Ostrovsky and achieved communication complexity of n ϵ {\displaystyle n^{\epsilon }} for any ϵ {\displaystyle \epsilon } , where n {\displaystyle n} is the number of bits in the database. The security of their scheme was based on the well-studied quadratic residuosity problem. In 1999, Christian Cachin, Silvio Micali and Markus Stadler achieved poly-logarithmic communication complexity. The security of their system is based on the phi-hiding assumption. In 2004, Helger Lipmaa achieved log-squared communication complexity O ( ℓ log ⁡ n + k log 2 ⁡ n ) {\displaystyle O(\ell \log n+k\log ^{2}n)} , where ℓ {\displaystyle \ell } is the length of the strings and k {\displaystyle k} is the security parameter. The security of his system reduces to the semantic security of a length-flexible additively homomorphic cryptosystem like the Damgård–Jurik cryptosystem. In 2005 Craig Gentry and Zulfikar Ramzan achieved log-squared communication complexity which retrieves log-square (consecutive) bits of the database. The security of their scheme is also based on a variant of the Phi-hiding assumption. The communication rate was finally brought down to 1 {\displaystyle 1} by Aggelos Kiayias, Nikos Leonardos, Helger Lipmaa, Kateryna Pavlyk, Qiang Tang, in 2015. All previous sublinear-communication computational PIR protocol required linear computational complexity of Ω ( n ) {\displaystyle \Omega (n)} public-key operations. In 2009, Helger Lipmaa designed a computational PIR protocol with communication complexity O ( ℓ log ⁡ n + k log 2 ⁡ n ) {\displaystyle O(\ell \log n+k\log ^{2}n)} and worst-case computation of O ( n / log ⁡ n ) {\displaystyle O(n/\log n)} public-key operations. Amortization techniques that retrieve non-consecutive bits have been considered by Yuval Ishai, Eyal Kushilevitz, Rafail Ostrovsky and Amit Sahai. As shown by Ostrovsky and Skeith, the schemes by Kushilevitz and Ostrovsky and Lipmaa use similar ideas based on homomorphic encryption. The Kushilevitz and Ostrovsky protocol is based on the Goldwasser–Micali cryptosystem while the protocol by Lipmaa is based on the Damgård–Jurik cryptosystem.

Advances in information theoretic PIR Achieving information theoretic security requires the assumption that there are multiple non-cooperating servers, each having a copy of the database. Without this assumption, any information-theoretically secure PIR protocol requires an amount of communication that is at least the size of the database n. Multi-server PIR protocols tolerant of non-responsive or malicious/colluding servers are called robust or Byzantine robust respectively. These issues were first considered by Beimel and Stahl (2002). An ℓ-server system that can operate where only k of the servers respond, ν of the servers respond incorrectly, and which can withstand up to t colluding servers without revealing the client's query is called "t-private ν-Byzantine robust k-out-of-ℓ PIR" [DGH 2012]. In 2012, C. Devet, I. Goldberg, and N. Heninger (DGH 2012) proposed an optimally robust scheme that is Byzantine-robust to ν < k − t − 1 {\displaystyle \nu <k-t-1} which is the theoretical maximum value. It is based on an earlier protocol of Goldberg that uses Shamir's Secret Sharing to hide the query. Goldberg has released a C++ implementation on SourceForge.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Private information retrieval

Start with the simplest possible case. Write down what Private information retrieval claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Private information retrieval before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Private information retrieval ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Private information retrieval

In research
Private information retrieval appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Private information retrieval in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Private information retrieval is common in secondary-school and first-year university syllabi. It links to neighbouring topics Cryptographic primitives, Theory of cryptography, so understanding it makes those chapters shorter.
In everyday life
Look for Private information retrieval outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Private information retrieval in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Private information retrieval means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Private information retrieval out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Private information retrieval in simple terms?

In cryptography, a private information retrieval (PIR) protocol is a protocol that allows a user to retrieve an item from a server in possession of a database without revealing which item is retrieved. PIR is a weaker version of 1-out-of-n oblivious transfer, where it is also required that the user…

Why does Private information retrieval matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Private information retrieval?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Private information retrieval.

Tags

  • Cryptographic primitives
  • Theory of cryptography

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