344 research outputs found
More is Less: Perfectly Secure Oblivious Algorithms in the Multi-Server Setting
The problem of Oblivious RAM (ORAM) has traditionally been studied in a
single-server setting, but more recently the multi-server setting has also been
considered. Yet it is still unclear whether the multi-server setting has any
inherent advantages, e.g., whether the multi-server setting can be used to
achieve stronger security goals or provably better efficiency than is possible
in the single-server case.
In this work, we construct a perfectly secure 3-server ORAM scheme that
outperforms the best known single-server scheme by a logarithmic factor. In the
process, we also show, for the first time, that there exist specific algorithms
for which multiple servers can overcome known lower bounds in the single-server
setting.Comment: 36 pages, Accepted in Asiacrypt 201
MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture
Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results:
- any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs;
- assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - ? fraction of machines (for an arbitrarily small constant ?) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup.
As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques
PROPYLA: Privacy Preserving Long-Term Secure Storage
An increasing amount of sensitive information today is stored electronically
and a substantial part of this information (e.g., health records, tax data,
legal documents) must be retained over long time periods (e.g., several decades
or even centuries). When sensitive data is stored, then integrity and
confidentiality must be protected to ensure reliability and privacy. Commonly
used cryptographic schemes, however, are not designed for protecting data over
such long time periods. Recently, the first storage architecture combining
long-term integrity with long-term confidentiality protection was proposed
(AsiaCCS'17). However, the architecture only deals with a simplified storage
scenario where parts of the stored data cannot be accessed and verified
individually. If this is allowed, however, not only the data content itself,
but also the access pattern to the data (i.e., the information which data items
are accessed at which times) may be sensitive information. Here we present the
first long-term secure storage architecture that provides long-term access
pattern hiding security in addition to long-term integrity and long-term
confidentiality protection. To achieve this, we combine information-theoretic
secret sharing, renewable timestamps, and renewable commitments with an
information-theoretic oblivious random access machine. Our performance analysis
of the proposed architecture shows that achieving long-term integrity,
confidentiality, and access pattern hiding security is feasible.Comment: Few changes have been made compared to proceedings versio
Lower Bounds for Oblivious Near-Neighbor Search
We prove an lower bound on the dynamic
cell-probe complexity of statistically
approximate-near-neighbor search () over the -dimensional
Hamming cube. For the natural setting of , our result
implies an lower bound, which is a quadratic
improvement over the highest (non-oblivious) cell-probe lower bound for
. This is the first super-logarithmic
lower bound for against general (non black-box) data structures.
We also show that any oblivious data structure for
decomposable search problems (like ) can be obliviously dynamized
with overhead in update and query time, strengthening a classic
result of Bentley and Saxe (Algorithmica, 1980).Comment: 28 page
Runtime protection via dataļ¬ow flattening
Software running on an open architecture, such as the PC, is vulnerable to inspection and modiļ¬cation. Since software may process valuable or sensitive information, many defenses against data analysis and modiļ¬cation have been proposed. This paper complements existing work and focuses on hiding data location throughout program execution. To achieve this, we combine three techniques: (i) periodic reordering of the heap, (ii) migrating local variables from the stack to the heap and (iii) pointer scrambling. By essentialy flattening the dataflow graph of the program, the techniques serve to complicate static dataflow analysis and dynamic data tracking. Our methodology can be viewed as a data-oriented analogue of control-flow flattening techniques. Dataflow flattening is useful in practical scenarios like DRM, information-flow protection, and exploit resistance. Our prototype implementation compiles C programs into a binary for which every access to the heap is redirected through a memory management unit. Stack-based variables may be migrated to the heap, while pointer accesses and arithmetic may be scrambled and redirected. We evaluate our approach experimentally on the SPEC CPU2006 benchmark suit
Mix-ORAM: Using Delegated Shuffles
Oblivious RAM (ORAM) is a key technology for providing private storage and querying on untrusted machines but is commonly seen as impractical due to the high and recurring overhead of the re-randomization, called the eviction, the client incurs. We propose in this work to securely delegate the eviction to semi-trusted third parties to enable any client to accede the ORAM technology and present four different designs inspired by mix-net technologies with reasonable periodic costs
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