Computable Jordan Decomposition of Linear Continuous Functionals on C[0;1]C[0;1]

By the Riesz representation theorem using the Riemann-Stieltjes integral, linear continuous functionals on the set of continuous functions from the unit interval into the reals can either be characterized by functions of bounded variation from the unit interval into the reals, or by signed measures on the Borel-subsets. Each of these objects has an (even minimal) Jordan decomposition into non-negative or non-decreasing objects. Using the representation approach to computable analysis, a computable version of the Riesz representation theorem has been proved by Jafarikhah, Lu and Weihrauch. In this article we extend this result. We study the computable relation between three Banach spaces, the space of linear continuous functionals with operator norm, the space of (normalized) functions of bounded variation with total variation norm, and the space of bounded signed Borel measures with variation norm. We introduce natural representations for defining computability. We prove that the canonical linear bijections between these spaces and their inverses are computable. We also prove that Jordan decomposition is computable on each of these spaces

Efficient Protocols for Multi-Party Computation

Secure Multi-Party Computation (MPC) allows a group of parties to compute a join function on their inputs without revealing any information beyond the result of the computation. We demonstrate secure function evaluation protocols for branching programs, where the communication complexity is linear in the size of the inputs, and polynomial in the security parameter. Our result is based on the circular security of the Paillier\u27s encryption scheme. Our work followed the breakthrough results by Boyle et al. [9; 11]. They presented a Homomorphic Secret Sharing scheme which allows the non-interactive computation of Branching Programs over shares of the secret inputs. Their protocol is based on the Decisional Diffie-Hellman Assumption. Additionally, we offer a verification technique to directly check correctness of the actual computation, rather than the absence of a potential error as in [9]. This results in fewer repetitions of the overall computation for a given error bound. We also use Paillier’s encryption as the underlying scheme of publicly perceptual hashing. Perceptual hashing allows the computation of a robust fingerprint of media files, such that the fingerprint can be used to detect the same object even if it has been modified in per- ceptually non-significant ways (e.g., compression). The robustness of such functions relies on the use of secret keys both during the computation and the detection phase. We present examples of publicly evaluatable perceptual hash functions which allow a user to compute the perceptual hash of an image using a public key, while only the detection algorithm will use the secret key. Our technique can be used to encourage users to submit intimate images to blacklist databases to stop those images from ever being posted online – indeed using a publicly evaluatable perceptual hash function the user can privately submit the fingerprint, without ever revealing the image. We present formal definitions for the security of perceptual hash, a general theoretical result that uses Fully Homomorphic Encryption, and a specific construction using Paillier’s encryption. For the latter we show via extensive implementation tests that the cryptographic overhead can be made minimal, resulting in a very efficient construction

The Riesz Representation Operator on the Dual of C[0; 1] is Computable

By the Riesz representation theorem, for every linear functional F : C[0; 1] → ℝ there is a function g : [0; 1] → ℝ of bounded variation such that A computable version is proved in [Lu and Weihrauch(2007)]: a function g can be computed from F and its norm, and F can be computed from g and an upper bound of its total variation. In this article we present a much more transparent proof. We first give a new proof of the classical theorem from which we then can derive the computable version easily. As in [Lu and Weihrauch(2007)] we use the framework of TTE, the representation approach for computable analysis, which allows to define natural concepts of computability for the operators under consideration