11,792 research outputs found
Single-Server Multi-Message Private Information Retrieval with Side Information
We study the problem of single-server multi-message private information
retrieval with side information. One user wants to recover out of
independent messages which are stored at a single server. The user initially
possesses a subset of messages as side information. The goal of the user is
to download the demand messages while not leaking any information about the
indices of these messages to the server. In this paper, we characterize the
minimum number of required transmissions. We also present the optimal linear
coding scheme which enables the user to download the demand messages and
preserves the privacy of their indices. Moreover, we show that the trivial MDS
coding scheme with transmissions is optimal if or .
This means if one wishes to privately download more than the square-root of the
number of files in the database, then one must effectively download the full
database (minus the side information), irrespective of the amount of side
information one has available.Comment: 12 pages, submitted to the 56th Allerton conferenc
Hiding Symbols and Functions: New Metrics and Constructions for Information-Theoretic Security
We present information-theoretic definitions and results for analyzing
symmetric-key encryption schemes beyond the perfect secrecy regime, i.e. when
perfect secrecy is not attained. We adopt two lines of analysis, one based on
lossless source coding, and another akin to rate-distortion theory. We start by
presenting a new information-theoretic metric for security, called symbol
secrecy, and derive associated fundamental bounds. We then introduce
list-source codes (LSCs), which are a general framework for mapping a key
length (entropy) to a list size that an eavesdropper has to resolve in order to
recover a secret message. We provide explicit constructions of LSCs, and
demonstrate that, when the source is uniformly distributed, the highest level
of symbol secrecy for a fixed key length can be achieved through a construction
based on minimum-distance separable (MDS) codes. Using an analysis related to
rate-distortion theory, we then show how symbol secrecy can be used to
determine the probability that an eavesdropper correctly reconstructs functions
of the original plaintext. We illustrate how these bounds can be applied to
characterize security properties of symmetric-key encryption schemes, and, in
particular, extend security claims based on symbol secrecy to a functional
setting.Comment: Submitted to IEEE Transactions on Information Theor
On Characterizing the Data Movement Complexity of Computational DAGs for Parallel Execution
Technology trends are making the cost of data movement increasingly dominant,
both in terms of energy and time, over the cost of performing arithmetic
operations in computer systems. The fundamental ratio of aggregate data
movement bandwidth to the total computational power (also referred to the
machine balance parameter) in parallel computer systems is decreasing. It is
there- fore of considerable importance to characterize the inherent data
movement requirements of parallel algorithms, so that the minimal architectural
balance parameters required to support it on future systems can be well
understood. In this paper, we develop an extension of the well-known red-blue
pebble game to develop lower bounds on the data movement complexity for the
parallel execution of computational directed acyclic graphs (CDAGs) on parallel
systems. We model multi-node multi-core parallel systems, with the total
physical memory distributed across the nodes (that are connected through some
interconnection network) and in a multi-level shared cache hierarchy for
processors within a node. We also develop new techniques for lower bound
characterization of non-homogeneous CDAGs. We demonstrate the use of the
methodology by analyzing the CDAGs of several numerical algorithms, to develop
lower bounds on data movement for their parallel execution
Towards Knowledge in the Cloud
Knowledge in the form of semantic data is becoming more and more ubiquitous, and the need for scalable, dynamic systems to support collaborative work with such distributed, heterogeneous knowledge arises. We extend the âdata in the cloudâ approach that is emerging today to âknowledge in the cloudâ, with support for handling semantic information, organizing and finding it efficiently and providing reasoning and quality support. Both the life sciences and emergency response fields are identified as strong potential beneficiaries of having âknowledge in the cloudâ
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