Practical Foundations of History Independence

Bajaj, Sumeet; Chakraborti, Anrin; Sion, Radu

doi:10.1109/tifs.2015.2491309

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…Independently, the recent work of [3] also considers a limited notion of history independence, called ∆history independence, parameterized with a function ∆ that describes the leakage. Our definition of history independence has a similar notion, where the leakage function ∆ captures the number of recent operations which may be revealed in a catastrophic attack.…”

Section: Related Workmentioning

confidence: 99%

A Practical Oblivious Map Data Structure with Secure Deletion and History Independence

Roche

Aviv

Choi

2016

2016 IEEE Symposium on Security and Privacy (SP)

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We present a new oblivious RAM that supports variable-sized storage blocks (vORAM), which is the first ORAM to allow varying block sizes without trivial padding. We also present a new historyindependent data structure (a HIRB tree) that can be stored within a vORAM. Together, this construction provides an efficient and practical oblivious data structure (ODS) for a key/value map, and goes further to provide an additional privacy guarantee as compared to prior ODS maps: even upon client compromise, deleted data and the history of old operations remain hidden to the attacker. We implement and measure the performance of our system using Amazon Web Services, and the single-operation time for a realistic database (up to 2 18 entries) is less than 1 second. This represents a 100x speed-up compared to the current best oblivious map data structure (which provides neither secure deletion nor history independence) by Wang et al. (CCS 14).

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Section: Related Workmentioning

confidence: 99%

A Practical Oblivious Map Data Structure with Secure Deletion and History Independence

Roche

Aviv

Choi

2016

2016 IEEE Symposium on Security and Privacy (SP)

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“…They present history-independent versions of hash tables and a general scheme for memory allocation that inspired parts of Boson. Later work [Bajaj et al 2016;Hartline et al 2002] elaborates the deterministic and statistical forms of history independence and provide additional schemes for history-independent data structure construction.…”

Section: Related Workmentioning

confidence: 99%

Tower: Data Structures in Quantum Superposition

Yuan,

Carbin

2022

Preprint

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Emerging quantum algorithms for problems such as element distinctness, subset sum, and closest pair demonstrate computational advantages by relying on abstract data structures. Practically realizing such an algorithm as a program for a quantum computer requires an efficient implementation of the data structure whose operations correspond to unitary operators that manipulate quantum superpositions of data.To correctly operate in superposition, an implementation must satisfy three properties -reversibility, history independence, and bounded-time execution. Standard implementations, such as representing an abstract set as a hash table, fail these properties, calling for tools to develop specialized implementations.In this work, we present Core Tower, the first language for quantum programming with random-access memory. Core Tower enables the developer to implement data structures as pointer-based, linked data. It features a reversible semantics enabling every valid program to be translated to a unitary quantum circuit.We present Boson, the first memory allocator that supports reversible, history-independent, and constanttime dynamic memory allocation in quantum superposition.We also present Tower, a language for quantum programming with inductive data structures. Tower features a type system that bounds all recursion using classical parameters.Using Tower, we implement Ground, the first quantum library of data structures, including lists, stacks, queues, strings, and sets. We provide the first executable implementation of sets that satisfies all three mandated properties of reversibility, history independence, and bounded-time execution.

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“…History Independence in Persistent Storage vs. RAM History independence has been vigorously explored in the context of data structures that reside in RAM [20,21,23,36,43,46,47,61] (see Section 1.4), but significantly less so in external memory. Although there is some theoretical work on history-independent on-disk data structures [31][32][33] and experimental work on history-independent file systems [10][11][12]56], the area is substantially less explored.…”

Section: History Independence In Persistent Storagementioning

confidence: 99%

Anti-Persistence on Persistent Storage

Bender

Berry

Johnson

et al. 2016

Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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We present history-independent alternatives to a B-tree, the primary indexing data structure used in databases. A data structure is history independent (HI) if it is impossible to deduce any information by examining the bit representation of the data structure that is not already available through the API.We show how to build a history-independent cache-oblivious Btree and a history-independent external-memory skip list. One of the main contributions is a data structure we build on the way-a history-independent packed-memory array (PMA). The PMA supports efficient range queries, one of the most important operations for answering database queries.Our HI PMA matches the asymptotic bounds of prior non-HI packed-memory arrays and sparse tables. Specifically, a PMA maintains a dynamic set of elements in sorted order in a linearsized array. Inserts and deletes take an amortized O(log 2 N ) element moves with high probability. Simple experiments with our implementation of HI PMAs corroborate our theoretical analysis. Comparisons to regular PMAs give preliminary indications that the practical cost of adding history-independence is not too large.Our HI cache-oblivious B-tree bounds match those of prior non- * HI cache-oblivious B-trees. Searches take O(log B N ) I/Os; inserts and deletes take O( log 2 N B + log B N ) amortized I/Os with high probability; and range queries returning k elements take O(log B N + k/B) I/Os. Our HI external-memory skip list achieves optimal bounds with high probability, analogous to in-memory skip lists: O(log B N ) I/Os for point queries and amortized O(log B N ) I/Os for inserts/deletes.Range queries returning k elements run in O(log B N + k/B) I/Os. In contrast, the best possible highprobability bounds for inserting into the folklore B-skip list, which promotes elements with probability 1/B, is just Θ(log N ) I/Os. This is no better than the bounds one gets from running an inmemory skip list in external memory.

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Practical Foundations of History Independence

Cited by 7 publications

References 16 publications

A Practical Oblivious Map Data Structure with Secure Deletion and History Independence

A Practical Oblivious Map Data Structure with Secure Deletion and History Independence

Tower: Data Structures in Quantum Superposition

Anti-Persistence on Persistent Storage

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