Actively Private and Correct MPC Scheme in t<n/2t < n/2 from Passively Secure Schemes with Small Overhead

Recently, several efforts to implement and use an unconditionally secure multi-party computation (MPC) scheme have been put into practice. These implementations are {\em passively} secure MPC schemes in which an adversary must follow the MPC schemes. Although passively secure MPC schemes are efficient, passive security has the strong restriction concerning the behavior of the adversary. We investigate how secure we can construct MPC schemes while maintaining comparable efficiency with the passive case, and propose a construction of an {\em actively} secure MPC scheme from passively secure ones. Our construction is secure in the $t < n/2$ setting, which is the same as the passively secure one. Our construction operates not only the theoretical minimal set for computing arbitrary circuits, that is, addition and multiplication, but also high-level operations such as shuffling and sorting. We do not use the broadcast channel in the construction. Therefore, privacy and correctness are achieved but {\em robustness} is absent; if the adversary cheats, a protocol may not be finished but anyone can detect the cheat (and may stop the protocol) without leaking secret information. Instead of this, our construction requires $O((c_B n + n^2)\kappa)$ communication that is comparable to one of the best known passively secure MPC schemes, $O((c_M n + n^2)\log n)$, where $\kappa$ denote the security parameter, $c_B$ denotes the sum of multiplication gates and high-level operations, and $c_M$ denotes the number of multiplication gates. Furthermore, we implemented our construction and confirmed that its efficiency is comparable to the current astest passively secure implementation

3-Party Secure Computation for RAMs: Optimal and Concretely Efficient

A distributed oblivious RAM (DORAM) is a method for accessing a secret-shared memory while hiding the accessed locations. DORAMs are the key tool for secure multiparty computation (MPC) for RAM programs that avoids expensive RAM-to-circuit transformations. We present new and improved 3-party DORAM protocols. For a logical memory of size $N$ and for each logical operation, our DORAM requires $O(\log N)$ local CPU computation steps. This is known to be asymptotically optimal. Our DORAM satisfies passive security in the honest majority setting. Our technique results with concretely-efficient protocols and does not use expensive cryptography (such as re-randomizable or homomorphic encryption). Specifically, our DORAM is 25X faster than the known most efficient DORAM in the same setting. Lastly, we extend our technique to handle malicious attackers at the expense of using slightly larger blocks (i.e., $\omega(\log^2 N)$ vs. $\Omega(\log N)$). To the best of our knowledge, this is the first concretely-efficient maliciously secure DORAM. Technically, our construction relies on a novel concretely-efficient 3-party oblivious permutation protocol. We combine it with efficient non-oblivious hashing techniques (i.e., Cuckoo hashing) to get a distributed oblivious hash table. From this, we build a full-fledged DORAM using a distributed variant of the hierarchical approach of Goldreich and Ostrovsky (J. ACM \u2796). These ideas, and especially the permutation protocol, are of independent interest

An Efficient Secure Three-Party Sorting Protocol with an Honest Majority

We present a novel three-party sorting protocol secure against passive adversaries in the honest majority setting. The protocol can be easily combined with other secure protocols which work on shared data, and thus enable different data analysis tasks, such as data deduplication, set intersection, and computing percentiles. The new sorting protocol is based on radix sort. It is asymptotically better compared to previous sorting protocols since it does not need to shuffle the entire length of the items after each comparison step. We further improve the concrete efficiency by using not only optimizations but also novel protocols, which are independent of interest. We implemented our sorting protocol with those optimizations and protocols. Our experiments show that our implementation is concretely fast. For example, sorting one million $20$-bit items takes 4.6 seconds in 1G connection. It enables a new set of applications on large-scale datasets since the known implementations handle thousands of items about 10 seconds

Secure Statistical Analysis on Multiple Datasets: Join and Group-By

We implement a secure platform for statistical analysis over multiple organizations and multiple datasets. We provide a suite of protocols for different variants of JOIN and GROUP-BY operations. JOIN allows combining data from multiple datasets based on a common column. GROUP-BY allows aggregating rows that have the same values in a column or a set of columns, and then apply some aggregation summary on the rows (such as sum, count, median, etc.). Both operations are fundamental tools for relational databases. One example use case of our platform is in data marketing in which an analyst would join purchase histories and membership information, and then obtain statistics, such as Which products were bought by people earning this much per annum? Both JOIN and GROUP-BY involve many variants, and we design protocols for several common procedures. In particular, we propose a novel group-by-median protocol that has not been known so far. Our protocols rely on sorting protocols, and work in the honest majority setting and against malicious adversaries. To the best of our knowledge, this is the first implementation of JOIN and GROUP-BY protocols secure against a malicious adversary

Efficient Secure Three-Party Sorting with Applications to Data Analysis and Heavy Hitters

We present a three-party sorting protocol secure against passive and active adversaries in the honest majority setting. The protocol can be easily combined with other secure protocols which work on shared data, and thus enable different data analysis tasks, such as private set intersection of shared data, deduplication, and the identification of heavy hitters. The new protocol computes a stable sort. It is based on radix sort and is asymptotically better than previous secure sorting protocols. It improves on previous radix sort protocols by not having to shuffle the entire length of the items after each comparison step. We implemented our sorting protocol with different optimizations and achieved concretely fast performance. For example, sorting one million items with 32-bit keys and 32-bit values takes less than 2 seconds with semi-honest security and about 3.5 seconds with malicious security. Finding the heavy hitters among hundreds of thousands of 256-bit values takes only a few seconds, compared to close to an hour in previous work

Study on Record Linkage of Anonymizied Data

Data anonymization is required before a big-data business can run effectively without compromising the privacy of personal information it uses. It is not trivial to choose the best algorithm to anonymize some given data securely for a given purpose. In accurately assessing the risk of data being compromised, there needs to be a balance between utility and security. Therefore, using common pseudo microdata, we propose a competition for the best anonymization and re-identification algorithm. The paper reported the result of the competition and the analysis on the effective of anonymization technique. The competition result reveals that there is a tradeoff between utility and security, and 20.9% records were re-identified in average