Linear Depth Integer-Wise Homomorphic Division

Part 3: CryptographyInternational audienceWe propose a secure integer-wise homomorphic division algorithm on fully homomorphic encryption schemes (FHE). For integer-wise algorithms, we encrypt plaintexts as integers without encoding them into bit values, while in bit-wise algorithms, plaintexts are encoded into binary and bit values are encrypted one by one. All the publicly available division algorithms are constructed in bit-wise style, and to the best of our knowledge there are no known integer-wise algorithm for secure division. We derive some empirical results on the FHE library HElib and show that our algorithm is 2.45x faster than the fastest bit-wise algorithm. We also show that the multiplicative depth of our algorithm is O(l), where l is the integer bit length, while that of existing division algorithms is $$O(l^2)$$. Furthermore, we generalise our secure division algorithm and propose a method for secure calculation of a general 2-variable function. The order of multiplicative depth of the algorithm, which is a main factor of the complexity of a FHE algorithm, is exactly the same as our secure division algorithm

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

Dodrant-Homomorphic Encryption for Cloud Databases using Table Lookup

Users of large commercial databases increasingly want to outsource their database operations to a cloud service providers, but guaranteeing the privacy of data in an outsourced database has become the major obstacle to this move. Encrypting all data solves the privacy issue, but makes many operations on the data impossible in the cloud, unless the service provider has the capacity to decrypt data temporarily. Homomorphic encryption would solve this issue, but despite great and on-going progress, it is still far from being operationally feasible. In 2015, we presented what we now call dodrant-homomorphic encryption, a method that encrypts numeric values deterministically using the additively homomorphic Paillier encryption and uses table lookup in order to implement multiplications. We discuss here the security implications of determinism and discuss options to avoid these pitfalls

Encrypted statistical machine learning: new privacy preserving methods

We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security constraints. We propose tailored algorithms for applying extremely random forests, involving a new cryptographic stochastic fraction estimator, and na\"{i}ve Bayes, involving a semi-parametric model for the class decision boundary, and show how they can be used to learn and predict from encrypted data. We demonstrate that these techniques perform competitively on a variety of classification data sets and provide detailed information about the computational practicalities of these and other FHE methods.Comment: 39 page