Abstract. A two-argument function is computed privately by two parties if after the computation, no party should know anything about the other inputs except for what he is able to deduce from his own input and the function value. In [1] Bar-Yehuda, Chor, Kushilevitz, and Orlitsky give a complete characterisation of two-argument functions which can be computed privately (in the information-theoretical sense) in the Honest-But-Curious model and study protocols for “non-private ” functions revealing as little information about the inputs as possible. The authors define a measure which determines for any function f the additional information E(f) required for computing f and claim that f is privately-computable if and only if E(f) = 0. In our paper we show that the characterisation is false: we give a privately-computable function f with E(f) = 0 and another function g with E(g) =0thatisnot privately-computable. Moreover, we show some rather unexpected and strange properties of the measure for additional information given by Bar-Yehuda et al. and we introduce an alternative measure. We show that for this new measure the minimal leakage of information of randomized and deterministic protocols are equal. Finally, we present some general relations between the information gain of an optimal protocol and the communication complexity of a function. 1
Abstract. A two-argument function is computed privately by two parties if after the computation, no party should know anything about the other inputs except for what he is able to deduce from his own input and the function value. In [1] Bar-Yehuda, Chor, Kushilevitz, and Orlitsky give a complete characterisation of two-argument functions which can be computed privately (in the information-theoretical sense) in the Honest-But-Curious model and study protocols for “non-private ” functions revealing as little information about the inputs as possible. The authors define a measure which determines for any function f the additional information E(f) required for computing f and claim that f is privately-computable if and only if E(f) = 0. In our paper we show that the characterisation is false: we give a privately-computable function f with E(f) = 0 and another function g with E(g) =0thatisnot privately-computable. Moreover, we show some rather unexpected and strange properties of the measure for additional information given by Bar-Yehuda et al. and we introduce an alternative measure. We show that for this new measure the minimal leakage of information of randomized and deterministic protocols are equal. Finally, we present some general relations between the information gain of an optimal protocol and the communication complexity of a function. 1
Each of two parties $P_\mathcal{X} $ and $P_\matcal{Y} $ holds an n-bit input, x and y, respectively. They wish to privately compute the value of f(x,y). That is, $P_\mathcal{X} $ should not learn any additional information about y (in the information-theoretic sense) other than what follows from its input x and the function value f(x,y), and similarly, $P_\mathcal{Y} $ should not learn any additional information about x. In this paper, the two following basic questions in the theory of private computations are considered: 1. Which functions can be privately computed? 2. What is the communication complexity of protocols that privately compute a function f (in the case that such protocols exist)? A complete combinatorial characterization of privately computable functions is given. This characterization is used to derive tight bounds on the rounds complexity of any privately computable function and to design optimal private protocols that compute these functions. It is shown that for every 1≤g(n)≤2⋅(2n−1) there are functions that can be privately computed with g(n)-rounds of communication, but not with (g(n)−1)-rounds of communication. This implies that the communication costs of private protocols can be exponentially higher than the communication costs of nonprivate protocols. Interestingly, randomization helps neither to increase the set of privately computable functions, nor to improve the rounds complexity of these functions.
Two parties, each holding one input of a two-variable function, communicate in order to determine the value of the function. Each party wants to expose as little of its input as possible to the other party. The authors prove tight bounds on the minimum amount of information about the individual inputs that must be revealed in the computation of most functions and of some specific ones. They also show that a computation that reveals little information about the individual inputs may require many more message exchanges than a more revealing computation.>
We define the notions of reducibility and completeness in multi-party private computations. Let g be an n-argument function. We say that a function f is reducible to g if n honest-but-curious players can compute the function f n-privately, given a black-box for g (for which they secretly give inputs and get the result of operating g on these inputs). We say that g is complete (for multi-party private computations) if every function f is reducible to g. In this paper, we characterize the complete Boolean functions: we show that a Boolean function g is complete if and only if g itself cannot be computed n-privately (when there is no black-box available). Namely, for Boolean functions, the notions of completeness and n-privacy are complementary. This characterization gives a huge collection of complete functions (any non-private Boolean function!) compared to very few examples given (implicitly) in previous work. On the other hand, for non-Boolean functions, we show that these two notions are not complementary. Our results can be viewed as a generalization (for multi-party protocols and for (n/spl ges/2)-argument functions) of the two-party case, where it was known that Oblivious Transfer protocol (and its variants) are complete.>
In secure multiparty computation (MPC), mutually distrusting users collaborate to compute a function of their private data without revealing any additional information about their data to the other users. While it is known that information theoretically secure MPC is possible among n users having access to private randomness and are pairwise connected by secure, noiseless, and bidirectional links against the collusion of less than n/2 users (in the honest-but-curious model; the threshold is n/3 in the malicious model), relatively little is known about the communication and randomness complexity of secure computation, i.e., the amount of communication and randomness required to compute securely. In this paper, we employ information theoretic techniques to obtain lower bounds on communication and randomness complexity of secure MPC. We restrict ourselves to a concrete interactive setting involving three users under which all functions are securely computable against corruption of individual users in the honest-but-curious model. We derive lower bounds for both the perfect security case (i.e., zero-error and no leakage of information) and asymptotic security (where the probability of error and information leakage vanish as block-length goes to ∞). Our techniques include the use of a data processing inequality for residual information (i.e., the gap between mutual information and Gács-Körner common information), a new information inequality for three-user protocols, and the idea of distribution switching by which lower bounds computed under certain worst case scenarios can be shown to apply for the general case. Our lower bounds are shown to be tight for various functions of interest. In particular, we show concrete functions which have communication-ideal protocols, i.e., which achieve the minimum communication simultaneously on all links in the network. Also, we obtain the first explicit example of a function that incurs a higher communication cost than the input length, in the secure computation model of Feige et al. (26th Annual ACM Symposium on Theory of Computing, 1994), who had shown that such functions exist. We also show that our communication bounds imply tight lower bounds on the amount of randomness required by MPC protocols for many interesting functions.
We introduce a new information-theoretic measure, which we call Public Information Complexity (PIC), as a tool for the study of multi-party computation protocols, and of quantities such as their communication complexity, or the amount of randomness they require in the context of information-theoretic private computations. We are able to use this measure directly in the natural asynchronous message-passing peer-to-peer model and show a number of interesting properties and applications of our new notion: The Public Information Complexity is a lower bound on the Communication Complexity and an upper bound on the Information Complexity; the difference between the Public Information Complexity and the Information Complexity provides a lower bound on the amount of randomness used in a protocol; any communication protocol can be compressed to its Public Information Cost; and an explicit calculation of the zero-error Public Information Complexity of the k -party, n -bit Parity function, where a player outputs the bitwise parity of the inputs. The latter result also establishes that the amount of randomness needed by a private protocol that computes this function is Ω ( n ).
Abstract. There are protocols to privately evaluate any function in the passive (honest-but-curious) setting assuming that the honest nodes are in majority. For some specific functions, protocols are known which remain secure even without an honest majority. The seminal work by Chor and Kushilevitz [7] gave a complete characterization of Boolean functions, showing that each Boolean function either requires an honest majority, or is such that it can be privately evaluated regardless of the number of colluding nodes. The problem of discovering the threshold for secure evaluation of more general functions remains an open problem. Towards a resolution, we provide a complete characterization of the security threshold for functions with three different outputs. Surprisingly, the zero-one law for Boolean functions extends to Z3, meaning that each function with range Z3 either requires honest majority or tolerates up to n colluding nodes. 1