Transaction Propagation on Permissionless Blockchains: Incentive and Routing Mechanisms

Existing permissionless blockchain solutions rely on peer-to-peer propagation mechanisms, where nodes in a network transfer transaction they received to their neighbors. Unfortunately, there is no explicit incentive for such transaction propagation. Therefore, existing propagation mechanisms will not be sustainable in a fully decentralized blockchain with rational nodes. In this work, we formally define the problem of incentivizing nodes for transaction propagation. We propose an incentive mechanism where each node involved in the propagation of a transaction receives a share of the transaction fee. We also show that our proposal is Sybil-proof. Furthermore, we combine the incentive mechanism with smart routing to reduce the communication and storage costs at the same time. The proposed routing mechanism reduces the redundant transaction propagation from the size of the network to a factor of average shortest path length. The routing mechanism is built upon a specific type of consensus protocol where the round leader who creates the transaction block is known in advance. Note that our routing mechanism is a generic one and can be adopted independently from the incentive mechanism.Comment: 2018 Crypto Valley Conference on Blockchain Technolog

Privacy enhanced recommender system

Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data and generate recommendations by processing them under encryption. With this approach, the service provider learns no information on any user’s preferences or the recommendations made. The proposed method is based on homomorphic encryption schemes and secure multiparty computation (MPC) techniques. The overhead of working in the encrypted domain is minimized by packing data as shown in the complexity analysis

An Adaptive Order-Statistic Noise Filter For Gamma-Corrected Image Sequences

Original video signals are often corrupted by a certain amount of noise originating from the camera electronics. As a result of the gamma correction in cameras, the observed noise is signal dependent. In this correspondence we present a spatio-temporal order-statistic (OS) noise filter that takes into account the gamma correction in the camera. The calculation of the filter coefficients requires higher-order order-statistics (HOOS) of the noise process. We make use of a range test (RT) to determine locally from which neighboring signal values an estimate should be formed. The noise filter that we arrive at is adaptive and computationally efficient. IEEE Transactions on Image Processing 1997 -1- 1. INTRODUCTION Most digital video signals that we consider as being the original and perfect recordings of a natural scene, are often corrupted by a certain amount of noise. Surprisingly, the amount of noise in original video signals is much higher than one would expect from the quantization..

using temporally extended Differential Energy Watermarking (DEW)

Low bit-rate video watermarkin