We propose the first fully homomorphic encryption scheme, solving an old open problem. Such a scheme allows one to compute arbitrary functions over encrypted data without the decryption key—i.e., given encryptions E(m1), ..., E( mt) of m1, ..., m t, one can efficiently compute a compact ciphertext that encrypts f(m1, ..., m t) for any efficiently computable function f. 
Fully homomorphic encryption has numerous applications. For example, it enables encrypted search engine queries—i.e., a search engine can give you a succinct encrypted answer to your (boolean) query without even knowing what your query was. It also enables searching on encrypted data; you can store your encrypted data on a remote server, and later have the server retrieve only files that (when decrypted) satisfy some boolean constraint, even though the server cannot decrypt the files on its own. More broadly, it improves the efficiency of secure multiparty computation. 
In our solution, we begin by designing a somewhat homomorphic "boostrappable" encryption scheme that works when the function f is the scheme's own decryption function. We then show how, through recursive self-embedding, bootstrappable encryption gives fully homomorphic encryption.

A fully homomorphic encryption scheme

The unreasonable effectiveness of quantum computing is founded on coherent quantum superposition or entanglement which allows a large number of calculations to be performed simultaneously. This coherence is lost as a quantum system interacts with its environment. In the present paper the problem of finding quantum-error-correcting codes is transformed into one of finding additive codes over the field GF(4) which are self-orthogonal with respect to a certain trace inner product. Many new codes and new bounds are presented, as well as a table of upper and lower bounds on such codes of length up to 30 qubits.

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Quantum error correction via codes over GF(4)

The prospect of outsourcing an increasing amount of data storage and management to cloud services raises many new privacy concerns for individuals and businesses alike. The privacy concerns can be satisfactorily addressed if users encrypt the data they send to the cloud. If the encryption scheme is homomorphic, the cloud can still perform meaningful computations on the data, even though it is encrypted.In fact, we now know a number of constructions of fully homomorphic encryption schemes that allow arbitrary computation on encrypted data. In the last two years, solutions for fully homomorphic encryption have been proposed and improved upon, but it is hard to ignore the elephant in the room, namely efficiency -- can homomorphic encryption ever be efficient enough to be practical? Certainly, it seems that all known fully homomorphic encryption schemes have a long way to go before they can be used in practice. Given this state of affairs, our contribution is two-fold.First, we exhibit a number of real-world applications, in the medical, financial, and the advertising domains, which require only that the encryption scheme is "somewhat" homomorphic. Somewhat homomorphic encryption schemes, which support a limited number of homomorphic operations, can be much faster, and more compact than fully homomorphic encryption schemes.Secondly, we show a proof-of-concept implementation of the recent somewhat homomorphic encryption scheme of Brakerski and Vaikuntanathan, whose security relies on the "ring learning with errors" (Ring LWE) problem. The scheme is very efficient, and has reasonably short ciphertexts. Our unoptimized implementation in magma enjoys comparable efficiency to even optimized pairing-based schemes with the same level of security and homomorphic capacity. We also show a number of application-specific optimizations to the encryption scheme, most notably the ability to convert between different message encodings in a ciphertext.

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Can homomorphic encryption be practical

We review recent progress in developing potential energy and dipole moment surfaces for polyatomic systems with up to 10 atoms. The emphasis is on global linear least squares fitting of tens of thousands of scattered ab initio energies using a special, compact fitting basis of permutationally invariant polynomials in Morse-type variables of all the internuclear distances. The computational mathematics underlying this approach is reviewed first, followed by a review of the practical approaches used to obtain the data for the fits. A straightforward symmetrization approach is also given, mainly for pedagogical purposes. The methods are illustrated for potential energy surfaces for , (H2O)2 and CH3CHO. The relationship of this approach to other approaches is also briefly reviewed.

Permutationally invariant potential energy surfaces in high dimensionality

Preface Part I. Introduction to the Four Themes: 1. Statistics L. Pachter and B. Sturmfels 2. Computation L. Pachter and B. Sturmfels 3. Algebra L. Pachter and B. Sturmfels 4. Biology L. Pachter and B. Sturmfels Part II. Studies on the Four Themes: 5. Parametric inference R. Mihaescu 6. Polytope propagation on graphs M. Joswig 7. Parametric sequence alignment C. Dewey and K. Woods 8. Bounds for optimal sequence alignment S. Elizalde 9. Inference functions S. Elizalde 10. Geometry of Markov chains E. Kuo 11. Equations defining hidden Markov models N. Bray and J. Morton 12. The EM algorithm for hidden Markov models I. B. Hallgrimsdottir, A. Milowski and J. Yu 13. Homology mapping with Markov random fields A. Caspi 14. Mutagenetic tree models N. Beerenwinkel and M. Drton 15. Catalog of small trees M. Casanellas, L. Garcia and S. Sullivant 16. The strand symmetric model M. Casanellas and S. Sullivant 17. Extending statistical models from trees to splits graphs D. Bryant 18. Small trees and generalized neighbor-joining M. Contois and D. Levy 19. Tree construction using Singular Value Decomposition N. Eriksson 20. Applications of interval methods to phylogenetics R. Sainudiin and R. Yoshida 21. Analysis of point mutations in vertebrate genomes J. Al-Aidroos and S. Snir 22. Ultra-conserved elements in vertebrate genomes M. Drton, N. Eriksson and G. Leung Index.

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Algebraic Statistics for Computational Biology

The MAGMA algebra system I: the user language

Algebraic Programming with Magma I: An Introduction to the Magma Language

This book, written in two volumes, is an introductory manual for Magma, a programming language designed for the investigation of algebraic, geometric and combinatorial structures, or "magmas". It presumes no knowledge of computer programming, and its examples are chosen to illustrate language and algorithmic features as simply as possible. Magma is a sophisticated tool for experimentation, education, and computer-aided proof, useful for both students and professional mathematicians. Volume I explains the language and user environment in detail, whereas Volume II deals with the major algebraic, geometrical and combinatorial structures implemented in the system.

Catherine Playoust

Papers

The MAGMA algebra system I: the user language

Algebraic Programming with Magma I: An Introduction to the Magma Language

Algebraic Programming with Magma II: An Introduction to the Magma Categories

Using the Magma computer algebra system in abstract algebra courses