Consider a network of k parties, each holding a long sequence of n entries (a database), with minimum vertex-cut greater than t. We show that any empirical statistic across the network of databases can be computed by each party with perfect privacy, against any set of t < k/2 passively colluding parties, such that the worst-case distortion and communication cost (in bits per database entry) both go to zero as n, the number of entries in the databases, goes to infinity. This is based on combining a striking dimensionality reduction result for random sampling with unconditionally secure multi-party computation protocols.
Consider a network of k parties, each holding a long sequence of n entries (a database), with minimum vertex-cut greater than t. We show that any empirical statistic across the network of databases can be computed by each party with perfect privacy, against any set of t < k/2 passively colluding parties, such that the worst-case distortion and communication cost (in bits per database entry) both go to zero as n, the number of entries in the databases, goes to infinity. This is based on combining a striking dimensionality reduction result for random sampling with unconditionally secure multi-party computation protocols.
Consider a network of k parties, each holding a long sequence of n entries (a database), with minimum vertex-cut greater than t. We show that any empirical statistic across the network of databases can be computed by each party with perfect privacy, against any set of t ¡ k=2 passively colluding parties, such that the worst-case distortion and communication cost (in bits per database entry) both go to zero as n, the number of entries in the databases, goes to infinity. This is based on combining a striking dimensionality reduction result for random sampling with unconditionally secure multi-party computation protocols. arXiv.org
We propose a novel distortion-theoretic approach to a secure three-party computation problem. Alice and Bob have deterministic sequences, and Charlie wishes to compute a normalized sum-type function of those sequences. We construct three-party protocols that allow Charlie to compute the function with arbitrarily high accuracy, while maintaining unconditional privacy for Alice and Bob and achieving vanishing communication cost. This work leverages a striking dimensionality reduction that allows a high accuracy estimate to be produced from only a random subsampling of the sequences. The worst-case distortion of the estimate, across all arbitrary deterministic sequences of any length, is independent of the dimensionality (length) of the sequences and proportional to inverse square root of the number of samples that the estimate is based upon.
In the problem of secure multi-party sampling, n parties wish to securely sample an n-variate joint distribution, with each party receiving a sample of one of the correlated variables. The objective is to correctly produce the samples using a distributed message passing protocol, while maintaining privacy against a coalition of passively cheating parties. In the two-party case, we fully characterize the joint distributions that can be securely sampled under perfect correctness and privacy requirements as well as under weakened correctness and privacy requirements. Furthermore, we show that the distributions that can be securely sampled can be produced with a protocol that only uses one round of unidirectional communication. For the n-party case, any distribution can be securely sampled with privacy against a strict minority coalition, due to well-known results in secure multi-party computation. However, when privacy against a majority coalition is required, not all distributions can be securely sampled. We give necessary conditions and sufficient conditions for distributions that can be securely sampled. However, the exact characterization of the distributions that can be securely sampled remains open.
In the problem of Secure Multi-party Computation, the objective is to design a protocol that allows a group of parties to securely compute functions of their collective private data, while ensuring that no party reveals any more information about its private data other than what must be inherently revealed by the computation results (privacy) and that no parties can disrupt or influence the computation results beyond the effects of changing their input data (correctness and consistency). Information theoretic approaches toward this problem, that provide provable (unconditional) security guarantees even against adversarial parties that have unbounded computational power, have established that general computation is possible in a variety of scenarios. However, these general solutions are not always efficient or finely tuned to the requirements of specific problems and applications.
The focus of this thesis is on the development of information theoretic approaches for multi-party computation applications with the common theme of secure and efficient computation and inference over a distributed data network. Specific applications include: 1) private information retrieval, where the objective is to privately retrieve data without revealing what was selected; 2) secure statistical analysis, the problem of extracting statistics without revealing anything else about the underlying data; 3) secure sampling, which is the secure generation of new data with a given distribution; and 4) secure authentication, where the identity of a party needs to authenticated via inference on his/her credentials and stored registration data.
The contributions of this thesis toward addressing these applications include the following: The development of an Oblivious Transfer (OT) protocol, applicable to private information retrieval, that trades off a small amount of privacy while being several times more efficient than other OT protocols. The efficiency of general se- cure two-party computation via OT realized from noisy channels is also analyzed. A technique for approximate secure multi-party computation applicable to secure statistical analysis of large scale distributed databases is developed. By exploiting a dimensionality reduction and the structure of a broad class of functions based on the computed statistics, the feasibility of simultaneously achieving both arbitrarily high accuracy and arbitrarily high communication efficiency is demonstrated. The region of distributions that can be securely sampled in the two-party scenario is characterized. It is further established that for those distributions, it can be accomplished with a protocol that only sends one message. Inner and outer bounds on the region of distributions that can be securely sampled in the general multi-party scenario are also developed. A new three-party protocol, applicable to the problem of secure biometric authentication, that securely computes the Hamming distance even when one of the parties arbitrarily deviates from the protocol is proposed and analyzed. A two-factor secure biometric authentication system that is robust against the compromise of registered biometric data, allowing for revocability and resistance against cross-enrollment attacks is also proposed.
This paper improves on the classical results in uncondition- ally secure multi-party computation among a set of n players, by consid- ering a model with three simultaneously occurring types of player cor- ruption: the adversary can actively corrupt (i.e. take full control over) up to ta players and, additionally, can passively corrupt (i.e. read the entire information of) up to tp players and fail-corrupt (i.e. stop the compu- tation of) up to tf other players. The classical results in multi-party computation are for the special cases of only passive (ta = tf =0 ) or only active (tp = tf = 0) corruption. In the passive case, every function can be computed securely if and only if tp <n = 2. In the active case, every function can be computed securely if and only if ta <n =3; when a broadcast channel is available, then this bound is ta <n =2. These bounds are tight. Strictly improving these results, one of our results states that, in addi- tion to tolerating ta <n =3 actively corrupted players, privacy can be guaranteed against every minority, thus tolerating additional tp n=6 passively corrupted players. These protocols require no broadcast and have an exponentially small failure probability. We further show that the bound t<n =2 for passive corruption holds even if the adversary is additionally allowed to make the passively corrupted players fail. Moreover, we characterize completely the achievable thresholds ta, tp and tf for four scenarios. Zero failure probability is achievable if and only if 3ta +2 tp + tf <n ; this holds whether or not a broadcast chan- nel is available. Exponentially small failure probability with a broadcast channel is achievable if and only if 2ta +2 tp +tf <n ; without broadcast, the additional condition 3ta + tf <n is necessary and sucient. In this corrected version, an error pointed out by Damg ard (Dam99) is corrected.
. This paper improves on the classical results in unconditionally secure multi-party computation among a set of n players, by considering a model with three simultaneously occurring types of player corruption: the adversary can actively corrupt (i.e. take full control over) up to ta players and, additionally, can passively corrupt (i.e. read the entire information of) up to tp players and fail-corrupt (i.e. stop the computation of) up to tf other players. The classical results in multi-party computation are for the special cases of only passive (ta = tf = 0) or only active (tp = tf = 0) corruption. In the passive case, every function can be computed securely if and only if tp ! n=2. In the active case, every function can be computed securely if and only if ta ! n=3; when a broadcast channel is available, then this bound is ta ! n=2. These bounds are tight. Strictly improving these results, one of our results states that, in addition to tolerating ta ! n=3 actively corrupted players, pr...