Private information and investment-q sensitivity:Evidence from new products

This study shows that a decline in investment efficiency due to information asymmetry may not necessarily devalue a firm but may enhance its value instead.Firms base their investment decisions on both public and private information.However,effectively transmitting private information to investors in a competitive market is quite challenging,as it leads to stock prices inadequately reflecting a firm’s true value.Simultaneously,firms must invest to achieve their business objectives,which results in a deviation between a firm’s actual investment level and the market’s expected investment level,thereby reducing investment-q sensitivity.However,this deviation does not imply a waste of societal resources;instead,it improves a firm’s core competitiveness in the product market.We consider the future launch of new products as a proxy for firm private information.We find that(1)private information is negatively associated with investment-q sensitivity,and that the greater the sales of the new product,the lower the investment-q sensitivity;(2)the negative effect is more pronounced for firms with higher revenue volatility and ownership concentration than for those with lower revenue volatility and ownership concentration.This study helps us to further understand how to evaluate firm investment efficiency from the perspective of private information and provides empirical evidence of the costs of the new product launch process.

Private membership test protocol with low communication complexity

We introduce a practical method to perform private membership tests.In this method,clients are able to test whether an item is in a set controlled by the server without revealing their query item to the server.After executing the queries,the content of the server's set remains secret.One use case for a private membership test is to check whether a file contains any malware by checking its signature against a database of malware samples in a privacy-preserving way.We apply the Bloom filter and the Cuckoo filter in the membership test procedure.In order to achieve privacy properties,we present a novel protocol based on some homomorphic encryption schemes.In our protocol,we rearrange the data in the set into N-dimensional hypercubes.We have implemented our method in a realistic scenario where a client of an anti-malware company wants to privately check whether a hash value of a given file is in the malware database of the company.The evaluation shows that our method is feasible for real-world applications.We also have tested the performance of our protocol for databases of different sizes and data structures with different dimensions:2-dimensional,3-dimensional,and 4-dimensional hypercubes.We present formulas to estimate the cost of computation and communication in our protocol.

Crisis of Management of Household Waste in Abidjan, The Answer of the Informal Private Precollectors in the Municipality of Yopougon (Côte d’Ivoire)

The present contribution aims at helping the Mayors to implement a less expensive and effective system of management of household refuse to Yopougon. Indeed, the incapacity of service providers to be assured the collection of the garbage and the failure of their systems of pre-collection favored the emergence of the informal private precollectors in the municipality of Yopougon. The regularity of the interventions of these new actors, the affordable cost of their services and the living environment daily freed the garbage incites the households to request them because they succeed better where service providers failed. Such an activity will have to be better organized because of supplier employment.

Asymmetric Consortium Blockchain and Homomorphically Polynomial-Based PIR for Secured Smart Parking Systems

In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.

Privacy Computing: Concept, Computing Framework, and Future Development Trends

With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private infor- mation in different information systems. However, problems such as the cask principle of preserving pri- vate information among different information systems and the dif culty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot pro- vide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a the- ory of privacy computing and a key technology system that includes a privacy computing framework, a formal de nition of privacy computing, four principles that should be followed in privacy computing, ffect algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.

Human-Centered A.I. and Security Primitives

The paper reviews how human-centered artificial intelligence and securityprimitive have influenced life in the modern world and how it’s useful inthe future. Human-centered A.I. has enhanced our capabilities by the wayof intelligence, human informed technology. It has created a technologythat has made machines and computer intelligently carry their function.The security primitive has enhanced the safety of the data and increasedaccessibility of data from anywhere regardless of the password is known.This has improved personalized customer activities and filled the gapbetween the human-machine. This has been successful due to the usageof heuristics which solve belowems by experimental, support vector machinewhich evaluates and group the data, natural language processingsystems which change speech to language. The results of this will lead toimage recognition, games, speech recognition, translation, and answeringquestions. In conclusion, human-centered A.I. and security primitivesis an advanced mode of technology that uses statistical mathematicalmodels that provides tools to perform certain work. The results keep onadvancing and spreading with years and it will be common in our lives.

Decoy-state method for quantum-key-distribution-based quantum private query

The quantum private query(QPQ)is a quantum solution for the symmetrically private information retrieval problem.We study the security of quantum-key-distribution-based QPQ with weak coherent pulses.The result shows that multiphoton pulses have posed a serious threat to the participant’s privacy in QPQ protocols.Then we propose a decoy-state method that can help the honest participant detect the attack by exploiting multiphoton pulses and improving the key distillation process to defend against such attack.The analysis demonstrates that our decoy-state method significantly improves the security of the QPQ with weak coherent pulses,which solves a major obstacle in the practical application of the QPQ.

Privacy Petri Net and Privacy Leak Software

Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software （PLS）. Nowadays, PLS has become a serious and challenging problem to cyber security. Previous methodologies are of two categories： one focuses on the outbound network traffic of the applications; the other dives into the inside information flow of the applications. We present an abstract model called Privacy Petri Net （PPN） which is more applicable to various applications and more intuitive and vivid to users. We apply our approach to both malware and suspicious applications in real world. The experimental result shows that our approach can effectively find categories, content, procedure, destination and severity of the private information leaks for the target software.

An Agent-Based Approach for Time-Series Momentum and Reversal

This paper proposes a novel agent-based model combining private information diffusion to explain time-series momentum and reversal.Private information transmission allows heterogeneous trading strategies coexist in the artificial market.The experiments reproduce momentum in short horizon and reversal in long horizon in the artificial financial market.Moreover,the authors also analyze how the private information contagion affects the momentum.Meanwhile,the authors find the significant price trend and excess volatility of volume when private information diffuses gradually.