Financial development, property rights, and growth

The authors analyze how property rights affect the allocation of firms'available resources among different types of assets. In particular, they investigate empirically for a large number of countries whether firms in environments with more secure property rights allocate available resources more toward intangible assets and consequentially grow faster. The authors find that improved asset allocation due to better property rights has an effect on growth in sectoral value added equal to improved access to financing arising from greater financial development. The results are robust, using various samples and specifications, including controlling for growth opportunities.Labor Policies,Economic Theory&Research,International Terrorism&Counterterrorism,Environmental Economics&Policies,Banks&Banking Reform,Environmental Economics&Policies,Economic Theory&Research,Banks&Banking Reform,Governance Indicators,Real&Intellectual Property Law

Modelling and Simulation of Handover in Light Fidelity (Li-Fi) Network

© 2018 IEEE. The demand of a faster and more secure wireless communication system leads to the development of a new and innovated network in future. Light Fidelity (Li-Fi) is being researched to provide a better wireless network communication. In this communication technology, light from Light Emitting Diodes (LEDs) has been used for data transmission. The purpose of this research work is to investigate the performance of handover algorithms in a Li-Fi network. Two handover algorithms are Closest Access Point (AP) (CAP) and Maximum Channel Gain (MCG). MATLAB simulation results are presented to evaluate those two types of handover algorithms and to show the impacts of UE's rotation and movement on handover performance

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively