We prove two classes of lower bounds on the communication complexity of information-theoretically secure multiparty computation. The first lower bound applies to perfect passive secure multiparty computation in the standard model with n = 2t+1 parties of which t are corrupted. We show a lower bound that applies to secure evaluation of any function, assuming that each party can choose to learn or not learn the output. Specifically, we show that there is a function H^* such that for any protocol that evaluates y_i = b_i ⋅ f(x₁,...,x_n) with perfect passive security (where b_i is a private boolean input), the total communication must be at least 1/2 ∑_{i = 1}ⁿ H_f^*(x_i) bits of information. The second lower bound applies to the perfect maliciously secure setting with n = 3t+1 parties. We show that for any n and all large enough S, there exists a reactive functionality F_S taking an S-bit string as input (and with short output) such that any protocol implementing F_S with perfect malicious security must communicate Ω(nS) bits. Since the functionalities we study can be implemented with linear size circuits, the result can equivalently be stated as follows: for any n and all large enough g ∈ ℕ there exists a reactive functionality F_C doing computation specified by a Boolean circuit C with g gates, where any perfectly secure protocol implementing F_C must communicate Ω(n g) bits. The results easily extends to constructing similar functionalities defined over any fixed finite field. Using known techniques, we also show an upper bound that matches the lower bound up to a constant factor (existing upper bounds are a factor lg n off for Boolean circuits). Both results also extend to the case where the threshold t is suboptimal. Namely if n = kt+s the bound is weakened by a factor O(s), which corresponds to known optimizations via packed secret-sharing.
Secure multiparty computation (MPC) is a cryptographic primitive which enables multiple parties to jointly compute a function without revealing any extra information on their private inputs. Bottleneck complexity is an efficiency measure that captures the load-balancing aspect of MPC protocols, defined as the maximum amount of communication required by any party. In this work, we study the problem of establishing lower bounds on the bottleneck complexity of MPC protocols. While the previously known techniques for lower bounding total communication complexity can also be applied to bottleneck complexity, they do not provide nontrivial bounds in the correlated randomness model, which is commonly assumed by existing protocols achieving low bottleneck complexity, or they are applied only to functions of limited practical interest. We propose several novel techniques for lower bounding the bottleneck complexity of MPC protocols. Our methods derive nontrivial lower bounds even in the correlated randomness model and apply to practically relevant functions including the sum function and threshold functions. Furthermore, our lower bounds demonstrate the optimality of some existing MPC protocols in terms of bottleneck complexity or the amount of correlated randomness.
Secure Multiparty Computation (MPC) is an expression encompassing cryptographic techniques that allow mutually distrustful parties to jointly perform computations over private data, ensuring correctness and preserving privacy despite malicious behavior. Guaranteed output is the strongest security goal for MPC: protocols with this property always deliver results. This is in contrast with weaker notions that allow malicious aborts. The seminal works of Ben-Or, Goldwasser, and Wigderson (STOC’88) and Rabin and Ben-Or (STOC’89) showed interactive protocols to compute any function with guaranteed output and perfect security if fewer than one third of parties are corrupted, or with statistical security (with a small error probability) if fewer than half are corrupted. These results hold when the set of participants is fixed throughout the execution. Ostrovsky and Yung’s proactive model (PODC’91) relaxes this assumption by allowing the set of corrupted parties to evolve over time, better capturing the dynamics of long-lived computations, where machines may be periodically compromised and recovered. The recent YOSO (You Only Speak Once) and fluid MPC models (CRYPTO’21) push this ambitious modelling even further by allowing for fully dynamic participation, with parties joining and leaving the computation at will. In this setting, known protocols with guaranteed output fall short of optimal resilience or rely on non-standard communication and adversarial assumptions. Matching the classical BGW and Rabin–Ben-Or bounds in these settings remained an open problem. We resolve these questions within the Layered MPC abstraction of David et al. (CRYPTO’23), in which the interaction pattern of a protocol is restricted to be a layered graph, meaning parties are partitioned into a sequence of disjoint sets (layers) and can send private and broadcast messages to parties in immediately subsequent layers. We match the BGW and Rabin and Ben-Or bounds in this model, fully characterizing the feasibility landscape of Layered MPC with guaranteed output. As a stepping stone towards our statistically secure protocol, we construct a more efficient and conceptually simpler computationally secure protocol relying on linear non-interactive commitments. Our results yield optimally-resilient and maximally-proactive MPC protocols with guaranteed output for both perfect and statistical security.
The private simultaneous messages (PSM) model is a simple variant of the secure multiparty computation (MPC). In the k-party PSM model, each party
P has a private input
xi for
i=1,…,k. For a function f, each
<p>Pi encrypts
xi into a message
mi with a random string r shared among
<p>P1,…,Pk, and sends
mi to the referee
R.R computes
f(x1,…,xk) from their respective messages
m1,…,mk. Then, R learns nothing from
m1,…,mk except for the output value
f(x1,…,xk). This simple model provides interesting cryptographic applications and is essential for understanding the intrinsic costs (e.g., of communication
∣m1∣+…+∣mk∣ and randomness
∣r∣) to achieve MPC. This study surveys recent results associated with the PSM and closely related models.
The private simultaneous messages model is a non-interactive version of the multiparty secure computation, which has been intensively studied to examine the communication cost of the secure computation. We consider its quantum counterpart, the private simultaneous quantum messages (PSQM) model, and examine the advantages of quantum communication and prior entanglement of this model. In the PSQM model,
k parties
P1,…,Pk initially share a common random string (or entangled states in a stronger setting), and they have private classical inputs
x1,…,xk. Every
Pi generates a quantum message from the private input
xi and the shared random string (entangled states), and then sends it to the referee
R. Receiving the messages,
R computes
F(x1,…,xk). Then,
R learns nothing except for
F(x1,…,xk) as the privacy condition. We obtain the following results for this PSQM model. (1) We demonstrate that the privacy condition inevitably increases the communication cost in the two-party PSQM model as well as in the classical case presented by Applebaum, Holenstein, Mishra, and Shayevitz. In particular, we prove a lower bound
(3−o(1))n of the communication complexity in two-party PSQM protocols with a shared random string for random Boolean functions of
2n-bit input, which is larger than the trivial upper bound
2n of the communication complexity without the privacy condition. (2) We demonstrate a factor two gap between the communication complexity of PSQM protocols with shared entangled states and with shared random strings by designing a multiparty PSQM protocol with shared entangled states for a total function that extends the two-party equality function. (3) We demonstrate an exponential gap between the communication complexity of PSQM protocols with shared entangled states and with shared random strings for a two-party partial function.
In the private simultaneous message (PSM) setting,
k players obtain inputs
xi∈{0,1}n and then each send messages to a referee, who should learn
f(x1,...,xk) but no other information about
(x1,...,xk). The PSM setting was introduced as a minimal model for secure multiparty computation and has connections to Boolean function complexity. In the quantum setting, PSM has been related to non-local quantum computation (NLQC). The communication and correlation cost of implementing PSM remains poorly understood. Here, we give new upper and lower bounds on the (quantum) PSM model. For lower bounds, we show: 1) Nečiporuk's measure lower bounds the entanglement required for
k-player quantum PSM with perfect correctness. This leads to quadratic lower bounds for explicit functions. 2) The rank of the communication matrix of
f(x1,x2) lower bounds 2-player quantum PSM with perfect privacy but imperfect correctness. This implies a previously unknown lower bound on classical PSM with imperfect correctness. When allowing quantum communication and shared entanglement, these are the first lower bounds on quantum PSM that make use of the privacy condition. For upper bounds, we show: 1) Letting
s be the size of a quantum circuit computing
f,
df be the circuit depth,
k the number of players,
n the number of bits received by each player, and
ε a correctness parameter, we obtain
PSMk∗(f)≤(kn+s)⋅logO(df)(s/ε). 2) The square of the Fourier 1 norm of
f,
∥f^∥12, upper bounds the classical PSM complexity,
PSM(f)≤O(∥f^∥12). In proving the first upper bound, we generalize existing
T-depth based techniques for NLQC from
2 to
k≥2 parties, and consider cases where the Clifford layers are restricted to having small light cones.