Unconditionally Secure Constant Round Multi-Party Computation for Equality, Comparison, Bits and Exponentiation

By Eike Kiltz. 2006

530 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Secure Evaluation of Quantized Neural Networks (2019, background): “Some of these protocols have been already studied in the setting of computation over ﬁelds (e.g. [8, 14]), however, over rings, some of these operations are not possible anymore and therefore some ring-compatible variants are required.”
Distributed Private Data Analysis: On Simultaneously Solving How and What (2008, methodology): “(7)One can use the techniques of [6] to avoid leaking these sums while maintaining a constant number of rounds, however the resulting protocol is less efficient.”
Obliv-C: A Language for Extensible Data-Oblivious Computation (2015, background): “There are well known examples [3] of how being able to generate secret random numbers (unknown to any party) can lead to significantly faster computation.”
Secure Computation on Floating Point Numbers (2013, background): “Prior work more closely related to ours primarily concentrates on techniques for integer computation and includes work on bit decomposition [19, 36, 42, 39], where a secret-shared value is converted to shares of the bits in its binary representation; comparison [19, 36, 26, 38], where the operands m”
Machine Learning Classification over Encrypted Data (2015, methodology): “These items have been well-studied previously; see [4], [30], [31], [32], [33], [19], [34] for comparison techniques and [35], [36], [37], [19] for techniques to compute dot products.”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “The reason for this is that the carry-lookahead adder [21] used in the conversion from [x] to 〈x〉 is very memory-intensive.”
Improved Primitives for Secure Multiparty Integer Computation (2010, methodology): “We refer to several known protocols as follows: PreMulC, for prefix multiplication [4]; KOrCS and PreOrCS, for k-ary and prefix OR [11].”
Multiparty Computation Goes Live (2008, methodology): “This enables comparison in constant-round with unconditional security[13] and also a logarithmic round solution that is more practical for the size of numbers we are interested in.”
Secure Computation with Fixed-Point Numbers (2010, methodology): “However, the solutions presented in [7,17] aim at providing perfect privacy and constant round complexity, while our protocols are designed for efficient secure computation with fixed-point numbers of typical size (≤ 128 bits).”
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, methodology): “Developing subprotocols for these tasks has been an active and fruitful line of research (e.g. [19], [30], [21], [15], [37]).”
Our Data, Ourselves: Privacy Via Distributed Noise Generation (2006, unknown): “[10], which requires n sharings and two multiplications per coin.”
Efficient, Oblivious Data Structures for MPC (2014, background): “[8] showed that a constant-round implementation is feasible.”
Generalizing the SPDZ Compiler For Other Protocols (2018, methodology): “In Table 5, we present the cost of our protocols versus those of [12], [27] and [30], when applied to the ring Z232 .”
Smart meter aggregation via secret-sharing (2013, methodology): “Our solution is based on extending these mechanisms with ideas from modern secret-sharing-based secure multi-party computation literature [4, 6].”