Ravi Kiran Raman, Roman Vaculín, Michael Hind, Sekou L. Remy · 9 authors
Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy. For instance, the OpenMalaria framework is a computationally-intensive simulation used by various non-governmental and governmental agencies to understand malarial disease spread and effectiveness of intervention strategies, and subsequently design healthcare policies. Given that such shared results form the basis of inferences drawn, technological solutions designed, and day-to-day policies drafted, it is essential that the computations are validated and trusted. In particular, in a multi-agent environment involving several independent computing agents, a notion of trust in results generated by peers is critical in facilitating transparency, accountability, and collaboration. Using a novel combination of distributed validation of atomic computation blocks and a blockchain-based immutable audits mechanism, this work proposes a universal framework for distributed trust in computations. In particular we address the scalaibility problem by reducing the storage and communication costs using a lossy compression scheme. This framework guarantees not only verifiability of final results, but also the validity of local computations, and its cost-benefit tradeoffs are studied using a synthetic example of training a neural network.
Gihan J. Mendis, Moein Sabounchi, Wei Jin, Rigoberto Roche
Deep learning algorithms have recently gained attention due to their inherent capabilities and the application opportunities that they provide. Two of the main reasons for the success of deep learning methods are the availability of processing power and big data. Both of these two are expensive and rare commodities that present limitations to the usage and implementation of deep learning. Decentralization of the processing and data is one of the most prevalent solutions for these issues. This paper proposes a cooperative decentralized deep learning architecture. The contributors can train deep learning models with private data and share them to the cooperative data-driven applications initiated elsewhere. Shared models are fused together to obtain a better model. In this work, the contributors can both design their own models or train the models provided by the initiator. In order to utilize an efficient decentralized learning algorithm, blockchain technology is incorporated as a method of creating an incentive-compatible market. In the proposed method, Ethereum blockchain's scripting capabilities are employed to devise a decentralized deep learning mechanism, which provides much higher, collective processing power and grants access to large amounts of data, which would be otherwise inaccessible. The technical description of the mechanism is described and the simulation results are presented.
In this article, the authors propose an approach for privacy preserving distributed clustering that assumes malicious model. In the literature, there do exist, numerous approaches that assume a semi honest model. However, such an assumption is, at best, reasonable in experimentations; rarely true in real world. Hence, it is essential to investigate approaches for privacy preservation using a malicious model. The authors use the Pederson's Verifiable Secret Sharing scheme ensuring the privacy using additively homomorphic secret sharing scheme. The trustworthiness of the data is assured using homomorphic commitments in Pederson's scheme. In addition, the authors propose two variants of the proposed approach - one for horizontally partitioned dataset and the other for vertically partitioned dataset. The experimental results show that the proposed approach is scalable in terms of dataset size. The authors also carry out experimentations to highlight the effectiveness of Verifiable Secret Sharing scheme against Zero Knowledge Proof scheme.
For the general problem of minimizing a convex function over a compact convex domain, we will investigate a simple iterative approximation algorithm based on the method by Frank & Wolfe 1956, that does not need projection steps in order to stay inside the optimization domain. Instead of a projection step, the linearized problem defined by a current subgradient is solved, which gives a step direction that will naturally stay in the domain. Our framework generalizes the sparse greedy algorithm of Frank & Wolfe and its primal-dual analysis by Clarkson 2010 (and the low-rank SDP approach by Hazan 2008) to arbitrary convex domains. We give a convergence proof guaranteeing ε-small duality gap after O(1/ε) iterations. The method allows us to understand the sparsity of approximate solutions for any l1-regularized convex optimization problem (and for optimization over the simplex), expressed as a function of the approximation quality. We obtain matching upper and lower bounds of Θ(1/ε) for the sparsity for l1-problems. The same bounds apply to low-rank semidefinite optimization with bounded trace, showing that rank O(1/ε) is best possible here as well. As another application, we obtain sparse matrices of O(1/ε) non-zero entries as ε-approximate solutions when optimizing any convex function over a class of diagonally dominant symmetric matrices. We show that our proposed first-order method also applies to nuclear norm and max-norm matrix optimization problems. For nuclear norm regularized optimization, such as matrix completion and low-rank recovery, we demonstrate the practical efficiency and scalability of our algorithm for large matrix problems, as e.g. the Netflix dataset. For general convex optimization over bounded matrix max-norm, our algorithm is the first with a convergence guarantee, to the best of our knowledge.
Zero-knowledge set is a primitive introduced by Micali, Rabin, and Kilian (FOCS 2003) which enables a prover to commit a set to a verifier, without revealing even the size of the set. Later the prover can give zero-knowledge proofs to convince the verifier of membership/nonmembership of elements in/not in the committed set. We present a new primitive called Statistically Hiding Sets (SHS), similar to zero-knowledge sets, but providing an information theoretic hiding guarantee. This is comparable to relaxing zero-knowledge proofs to witness independent proofs. More precisely, we continue to use the simulation paradigm for our definition, but do not require the simulator (nor the distinguisher) to be efficient. We present a new scheme for statistically hiding sets, which does not fit into the “Merkletree/mercurial-commitment” paradigm used for all zero-knowledge set constructions so far. This not only provides some efficiency gains compared to the best possible schemes in that paradigm, but also lets us provide statistical hiding, without the prover having to maintain growing amounts of state with each new proof; this is not known to be possible with the previous approach.
2 source records
Advanced Steganography and Watermarking Techniques
Rahul Jain, Alexandra Kolla, Gatis Midrijānis, Ben W. Reichardt
Let L be a language decided by a constant-round quantum Arthur-Merlin (QAM)\nprotocol with negligible soundness error and all but possibly the last message\nbeing classical. We prove that if this protocol is zero knowledge with a\nblack-box, quantum simulator S, then L in BQP. Our result also applies to any\nlanguage having a three-round quantum interactive proof (QIP), with all but\npossibly the last message being classical, with negligible soundness error and\na black-box quantum simulator.\n These results in particular make it unlikely that certain protocols can be\ncomposed in parallel in order to reduce soundness error, while maintaining zero\nknowledge with a black-box quantum simulator. They generalize analogous\nclassical results of Goldreich and Krawczyk (1990).\n Our proof goes via a reduction to quantum black-box search. We show that the\nexistence of a black-box quantum simulator for such protocols when L notin BQP\nwould imply an impossibly-good quantum search algorithm.\n