888 research outputs found
Two-Round Multiparty Secure Computation Minimizing Public Key Operations
We show new constructions of semi-honest and malicious two-round multiparty secure computation
protocols using only (a fixed) invocations of a two-round oblivious
transfer protocol (which use expensive public-key operations) and cheaper one-way function calls, where is the security parameter, is the number of parties, and is the circuit being computed. All previously known two-round multiparty secure computation protocols required expensive public-key operations
Secure Numerical and Logical Multi Party Operations
We derive algorithms for efficient secure numerical and logical operations
using a recently introduced scheme for secure multi-party
computation~\cite{sch15} in the semi-honest model ensuring statistical or
perfect security. To derive our algorithms for trigonometric functions, we use
basic mathematical laws in combination with properties of the additive
encryption scheme in a novel way. For division and logarithm we use a new
approach to compute a Taylor series at a fixed point for all numbers. All our
logical operations such as comparisons and large fan-in AND gates are perfectly
secure. Our empirical evaluation yields speed-ups of more than a factor of 100
for the evaluated operations compared to the state-of-the-art
Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare
Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare
Ad Hoc Multi-Input Functional Encryption
Consider sources that supply sensitive data to an aggregator. Standard encryption only hides the data from eavesdroppers, but using specialized encryption one can hope to hide the data (to the extent possible) from the aggregator itself. For flexibility and security, we envision schemes that allow sources to supply encrypted data, such that at any point a dynamically-chosen subset of sources can allow an agreed-upon joint function of their data to be computed by the aggregator. A primitive called multi-input functional encryption (MIFE), due to Goldwasser et al. (EUROCRYPT 2014), comes close, but has two main limitations:
- it requires trust in a third party, who is able to decrypt all the data, and
- it requires function arity to be fixed at setup time and to be equal to the number of parties.
To drop these limitations, we introduce a new notion of ad hoc MIFE. In our setting, each source generates its own public key and issues individual, function-specific secret keys to an aggregator. For successful decryption, an aggregator must obtain a separate key from each source whose ciphertext is being computed upon. The aggregator could obtain multiple such secret-keys from a user corresponding to functions of varying arity. For this primitive, we obtain the following results:
- We show that standard MIFE for general functions can be bootstrapped to ad hoc MIFE for free, i.e. without making any additional assumption.
- We provide a direct construction of ad hoc MIFE for the inner product functionality based on the Learning with Errors (LWE) assumption. This yields the first construction of this natural primitive based on a standard assumption.
At a technical level, our results are obtained by combining standard MIFE schemes and two-round secure multiparty computation (MPC) protocols in novel ways highlighting an interesting interplay between MIFE and two-round MPC
Conclave: secure multi-party computation on big data (extended TR)
Secure Multi-Party Computation (MPC) allows mutually distrusting parties to
run joint computations without revealing private data. Current MPC algorithms
scale poorly with data size, which makes MPC on "big data" prohibitively slow
and inhibits its practical use.
Many relational analytics queries can maintain MPC's end-to-end security
guarantee without using cryptographic MPC techniques for all operations.
Conclave is a query compiler that accelerates such queries by transforming them
into a combination of data-parallel, local cleartext processing and small MPC
steps. When parties trust others with specific subsets of the data, Conclave
applies new hybrid MPC-cleartext protocols to run additional steps outside of
MPC and improve scalability further.
Our Conclave prototype generates code for cleartext processing in Python and
Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave
scales to data sets between three and six orders of magnitude larger than
state-of-the-art MPC frameworks support on their own. Thanks to its hybrid
protocols, Conclave also substantially outperforms SMCQL, the most similar
existing system.Comment: Extended technical report for EuroSys 2019 pape
Semi-quantum communication: Protocols for key agreement, controlled secure direct communication and dialogue
Semi-quantum protocols that allow some of the users to remain classical are
proposed for a large class of problems associated with secure communication and
secure multiparty computation. Specifically, first time semi-quantum protocols
are proposed for key agreement, controlled deterministic secure communication
and dialogue, and it is shown that the semi-quantum protocols for controlled
deterministic secure communication and dialogue can be reduced to semi-quantum
protocols for e-commerce and private comparison (socialist millionaire
problem), respectively. Complementing with the earlier proposed semi-quantum
schemes for key distribution, secret sharing and deterministic secure
communication, set of schemes proposed here and subsequent discussions have
established that almost every secure communication and computation tasks that
can be performed using fully quantum protocols can also be performed in
semi-quantum manner. Further, it addresses a fundamental question in context of
a large number problems- how much quantumness is (how many quantum parties are)
required to perform a specific secure communication task? Some of the proposed
schemes are completely orthogonal-state-based, and thus, fundamentally
different from the existing semi-quantum schemes that are
conjugate-coding-based. Security, efficiency and applicability of the proposed
schemes have been discussed with appropriate importance.Comment: 19 pages 1 figur
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