Disclosure Of Private Information Research Articles

Peer-to-peer energy trading with decentralized bidirectional matching of multipreference community prosumers

Peer-to-peer (P2P) energy trading of community prosumers is effective at coordinating grid-connected distributed energy resources. The existing methods fail to address the multiple preferences of prosumers in energy trading, and the information privacy and autonomy of prosumers are not respected. These issues hinder the effective and applicable P2P energy trading of community prosumers. To bridge this gap, this paper proposes a decentralized bidirectional matching method (DBMM) based on multiattribute decision making (MADM) to match the P2P energy trades between sellers and buyers. This method enables the bidirectional evaluation, selection, and matching of buyers/sellers, allowing prosumers to make decisions on energy trading autonomously without disclosing private information. The numerical analysis indicates that compared with prevalent trading methods, the proposed DBMM is more cost-effective and generalized for handling multiple preferences while preserving information privacy and autonomy. In the four-preference case, the total revenue of the proposed DBMM is 32.30 % greater than that of the other compared methods. Furthermore, the computational efficiency and scalability of this method are also validated.

A novel quantum security multi-party extremum protocol in a d-dimensional quantum system

Secure multi-party extremum computation (SMEC) is a specific application scenario of secure multi-party computation, which allows multiple participants to compute the extremum of data without disclosing private information. The extremum includes maximum, minimum, sum of extremums, and difference of extremums. SMEC has wide applications in financial transactions, market analysis, sports events, healthcare, etc. Current protocol research mainly exists in the classical domain and cannot withstand quantum computing attacks. To address this issue, we propose a novel QSME protocol based on a d-dimensional quantum system, capable of computing the maximum and minimum values among multi-party data under unconditional security, and can compute the sum and difference of extremums without disclosing the maximum and minimum values, to adapt to complex application scenarios. The article proposes a coding method for a d-dimensional quantum system to further enhance security, provides correctness analysis, security analysis, robustness analysis, and comparative analysis, and proposes an experimental method for a d-dimensional quantum system to verify the effectiveness of the protocol, demonstrating strong practicality.

Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm's ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

The impact of a preopening session on subsequent trading: An experimental analysis

AbstractIn this study, we examine the influence of a preopening call market (CM) session on the subsequent continuous double auction (CDA) trading phase within a combined market structure (CMDA). We propose that the introduction of a CM phase before the CDA could potentially mitigate price volatility during the CDA by facilitating the disclosure of agents' private information through the preopening pricing phase. Our findings reveal a positive relationship between the preopening price and the prices traded during the subsequent phase, alongside a decreased deviation of prices from their fundamental value when high‐quality information is available. These results suggest that a well‐defined preopening phase has the potential to improve market efficiency.

Resilient energy management strategy in smart residential buildings considering price attack: An aggregative game perspective

Smart building techniques have drawn considerable attention for the potential to accommodate renewable energy resources and achieve local energy sustainability. However, information privacy, growing number of users and vulnerability of communication channels have led to energy management and cyber-security issues. In this paper, a cyber-attack resilient energy management system for smart residential buildings is proposed. An aggregative game is formulated to describe the non-cooperative interactions among smart buildings that are coupled through dynamic electricity pricing. The existence of the variational equilibrium is analyzed theoretically. Moreover, a semi-decentralized algorithm is employed to attain the equilibrium solution without disclosing private information of individual buildings. To detect potential price attacks, a cyber-attack detection mechanism is proposed by comparing the predicted and actual electricity costs and exchanged power based on the long short-term memory network model. Numerical simulations demonstrate the performance of the semi-decentralized algorithm in term of convergence speed and robustness against disturbances. Additionally, the effectiveness of the resilient energy management strategy is investigated.

Under the Hood of Activist Fraud Campaigns: Private Information Quality, Disclosure Incentives, and Stock Lending Dynamics

ABSTRACT Although activist short sellers can play a crucial role in fraud detection, they have come under scrutiny following accusations of systematically disseminating false negative information. We develop a framework delineating the roles of campaign-specific private information quality and short-selling dynamics in shaping disclosure incentives. We predict that the act of disclosure combined with pre-disclosure stock lending dynamics is informative about the quality of an activist’s private information. We find that increased pre-disclosure shorting intensity is associated with more negative post-disclosure returns, adverse media coverage, and consequential campaign outcomes, including auditor turnover, accounting restatements, class-action lawsuits, and performance-related delistings. Furthermore, elevated short-selling costs and risks magnify the association between pre-disclosure shorting intensity and post-disclosure underperformance. Finally, we examine V-shaped reversals and short covering following activists’ disclosures and find no evidence of systematic manipulation. We conclude that activists disclosing fraud allegations under their own names are discouraged from engaging in “short-and-distort” schemes. Data Availability: Data are available from the sources cited in the text. JEL Classifications: G12; G14; G23; M41.

Framework for smart contract blockchain in halal traceability, integrity, and transparency

Currently, the halalness of a product may only be determined by the halal certificate or halal label displayed on the product packaging. Simply put, several halal-related issues, like cross-contamination, logistical issues, halal counterfeiting, global halal acceptance, and so forth, keep coming up. To address the numerous issues raised above, it is believed that a framework that can support worldwide halal provisions is necessary. The objective of this research is to create a blockchain smart contract framework for halal transparency, integrity, and traceability. Blockchain technology along with smart contracts can solve classic tracing problems including data leakage, manipulation, and invisible data. Organizational consistency across the halal production process and the availability of raw materials that adhere to sharia laws are two more basic challenges with halal certification that blockchain and smart contracts can resolve. The usual traceability problems, such as data explosion, material cross-contamination between halal and haram, and revelation of private information, can all be resolved by smart contracts that make use of on-chain, off-chain, and electronic product code information services (EPCIS).

Braveheart: On the divergence of recommendations between normal and star analysts

This paper examines how conflicting recommendations from star analysts and normal analysts predict a firm's future stock price and operating performance. We find that when normal analysts issue bold negative recommendations that contradict the views of star analysts, firms that receive such negative recommendations experience worse stock returns and deteriorating fundamental performance over the following two years. Our study provides new insights into the disclosure of private information by financial analysts.

Самопрезентация пользователя в социальных сетях и проблема коллапса контекста

This article discusses the social problems of communication in social networks that provide a platform and tools for the formation of a virtual network identity. The features of the behavior of users of social networks and the tactics of their self-presentation when constructing a personal network profile are described. It is argued that in the process of social communication, people present different versions of themselves depending on their audience and adapting their own self-presentation to the perceived values and preferences of the interlocutors. The difficulties of online communication are described, where, unlike real interaction, the user does not have the opportunity to correlate his/ her behavior with the situational context and carry out differentiated self-presentation. The problems of blurring the boundaries of public and private in social networks and the loss of information privacy, which is a necessary condition for personal freedom and autonomy, are raised. It is argued that the disclosure of private information is an important component of maintaining social relations. It is noted that currently there is a transformation of cultural norms regulating the degree of openness-closeness and publicity-privacy. The concept of "context collapse" is introduced – a situation caused by the mixing of heterogeneous audiences with different social values, norms and expectations into one homogeneous group. The strategies are proposed to overcome the negative consequences of this situation: dividing the audience into several segments, self-censorship, forcing to avoid discussing controversial topics and adapting the user's language style, suggesting a reduction in language variability. The work was based on the methods of symbolic interactionism and I. Hoffman's theory of roles, which he used to build the dramatic theory of social interaction and self-presentation in social interaction.

The role of school climate on student disclosure of private information and cyberbullying: a comparison of regular and vocational school students in China.

Adolescents are experiencing an unprecedented cyber-saturated environment where the disclosure of private information should be approached with caution. This study aims to investigate the effects of school environment, including student support, teacher support, and opportunities for autonomy, on students' disclosure of private information and their experiences with cyberbullying. In September 2022, a total of 1,716 students (mean age = 14.60, SD = 1.35) from three regular and vocational schools in China participated in the survey. The results showed that 35.6% of the participants had experienced victimization by cyberbullying, and 12.6% had perpetrated cyberbullying. Vocational school students reported significantly higher rates of cyberbullying and lower levels of perceived school climate than students from regular school. Student support in the school environment was found to negatively affect both cyberbullying perpetration and victimization, with this impact appearing to be stronger in regular schools as compared to vocational schools. Opportunities for autonomy and the disclosure of private information were positively correlated with experiences of cyberbullying. This study introduces a novel perspective that perceived school climate influences adolescents' disclosure of private information and their involvement in cyberbullying. The findings could provide implications for future research and practices aimed at child protection in cyberspace.

Bargaining with Voluntary Disclosure and Endogenous Matching

We investigate a bargaining setting between an “informed” player, who has private information, and an “uninformed” player. The informed player has the option to truthfully disclose private information in two unique environments. In the first environment, the informed player is randomly matched with an uninformed player and, after matching, can voluntarily disclose private information prior to a negotiation taking place. In the second environment, the informed player can voluntarily disclose private information before any endogenous matching between players (and subsequent negotiation) takes place. We begin by examining these environments theoretically. When disclosure occurs after matching, we show that low informed types should disclose more often than high informed types to avoid disagreement. However, when disclosure occurs before matching, for a range of parameter values, we show that high informed types should disclose more than low informed types to secure a better match. We test these predictions in a controlled human-subjects experiment and verify many of the theoretical predictions. Among other results, we find that low informed types do indeed disclose their private information to avoid disagreement and that, when it is in their interest to do so, high informed types disclose their private information to secure a better match. Another key insight is that high uninformed types benefit from disclosure regardless of whether it occurs before or after matching. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03566 .

Knowledge-Related Policy Analysis in an Inference-Enabled Actor Model

People provide their information to distributed systems to receive the desired services. This information may be disclosed to the agents of the system as part of messages transmitted among them. As the agents of the system are smart, they can infer new information from their obtained information, that they may not be authorized to know. So preserving privacy in such systems is an important and yet challenging issue. We study the problem of analyzing the disclosure of private information in distributed asynchronous systems. Our approach to prevent private information disclosure is to require the system to follow knowledge-related policies defined for the system at design time. To achieve this, we construct a model of the system and assume the policies as the system properties and check whether these properties are satisfied in the system or not. In order to construct a model of the system, we extend the actor model, which is a well known reference model for distributed asynchronous systems, by enriching actors by the knowledge base and inference capability. As our knowledge-related policies should not be violated in any state of the system, we propose an efficient invariant model checking algorithm to verify the satisfaction of the policies in our actor model.

Sharing versus donating: The role of message framing in consumers' private information disclosure for societal benefits

AbstractAcross three studies, this research elucidates how message frames—sharing versus donating—can affect consumers' private information disclosure for societal benefits through perceived sacrifice. This effect is moderated by perceived importance of the cause. First, we demonstrate that the sharing (donating) frame can generate more (less) private information disclosure for societal benefits. Second, we examine the mechanism by which the sharing (donating) frame leads to less (more) perceived sacrifice and then more (less) information disclosure. Third, we introduce the perceived importance of the cause and find that for people who perceive the importance of the cause is high, the donating (sharing) frame leads to less (more) perceived sacrifice and stronger (weaker) intention for private information disclosure. This research has implications for marketers, consumers, and society as a whole.

Role of “Likes” and “Dislikes” in Influencing User Behaviors on Social Media

ABSTRACT Two recent changes on social media platforms include (1) allowing users to mask the number of “likes” on others’ posts, aimed at reducing social comparisons and consequent technology-mediated dangerous behaviors (TMDBs), such as disclosing private information, and (2) adding a “dislike” reaction, aimed at increasing engagement. Nevertheless, the effects of these changes on user behaviors are unclear. In this paper, we seek to address this gap by integrating risk-sensitivity theory (RST) and prospect theory. First, we explain that while masking others’ “likes” may reduce social comparison, based on the “homeostatic violation” concept, people also make internal comparisons to expectations. Undesirable deviations from users’ expectations of “likes” and “dislikes” (e.g., too few “likes” or too many “dislikes”) can motivate TMDBs that users believe can alleviate the undesirable deviations. Thus, we argue that, in addition to social comparison mechanisms, there is an internal comparison mechanism that can motivate TMDBs. We test these claims via five randomized controlled experiments (total n = 1,594). Results show that, beyond social comparison mechanisms, receiving too few “likes” or too many “dislikes,” compared to internal expectations, can motivate TMDBs. Moreover, we found that losses loom larger than gains as users are more likely to engage in TMDBs to avoid excess “dislikes” than to avoid deprivation of “likes.” These findings make novel contributions to social media research and practice by pointing to an internal comparison mechanism as a potent motivator of TMDBs beyond social comparison processes and to the higher “toxicity” of “dislikes” than “likes” in terms of inciting TMDBs on social media.

An Auction-Based Approach for Multi-Agent Uniform Parallel Machine Scheduling with Dynamic Jobs Arrival

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents. All agents are self-interested and rational with the aim of maximizing their own objectives, resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information. Within the context, a centralized scheduling approach is unfeasible, and a decentralized approach is considered to deal with the targeted problem. This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’ preferences under incomplete information. For this purpose, a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed. In the proposed approach, a dynamic auction procedure is established for dynamic jobs participating in a real-time auction, and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination. In addition, an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently. A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids. Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on large-scale problems with numerous consumer agents and jobs. A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

Tailoring Gradient Methods for Differentially Private Distributed Optimization

Decentralized optimization is gaining increased traction due to its widespread applications in large-scale machine learning and multi-agent systems. The same mechanism that enables its success, i.e., information sharing among participating agents, however, also leads to the disclosure of individual agents' private information, which is unacceptable when sensitive data are involved. As differential privacy is becoming a de facto standard for privacy preservation, recently results have emerged integrating differential privacy with distributed optimization. However, directly incorporating differential privacy design in existing distributed optimization approaches significantly compromises optimization accuracy. In this paper, we propose to redesign and tailor gradient methods for differentially-private distributed optimization, and propose two differential-privacy oriented gradient methods that can ensure both rigorous <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -differential privacy and optimality. The first algorithm is based on static-consensus based gradient methods, and the second algorithm is based on dynamic-consensus (gradient-tracking) based distributed optimization methods and, hence, is applicable to general directed interaction graph topologies. Both algorithms can simultaneously ensure almost sure convergence to an optimal solution and a finite privacy budget, even when the number of iterations goes to infinity. To our knowledge, this is the first time that both goals are achieved simultaneously. Numerical simulations using a distributed estimation problem and experimental results on a benchmark dataset confirm the effectiveness of the proposed approaches.

Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data

Federated learning (FL) has attracted widespread attention in the Internet of Things domain recently. With FL, multiple distributed devices can cooperatively train a global model by transmitting model updates without disclosing the original data. However, the distributed nature of FL makes it vulnerable to data poisoning attacks. In practice, malicious clients can launch the label-flipping attack by simply tampering with the labels of local data, thus causing the global model to misclassify the samples of a selected class as the target class. Although some defense mechanisms have been proposed, they rely on specific assumptions about data distribution, and their performance degrades significantly when the data on clients are non-IID. Besides, most existing methods require clients to upload model updates in plaintext so that the server can identify and remove the malicious updates. But, direct transmission of model updates may still reveal private information. Considering these issues, we develop a label-flipping-robust and privacy-preserving FL (LFR-PPFL) algorithm, which is applicable to both IID and non-IID data. We first propose a detection method based on temporal analysis on cosine similarity to distinguish malicious clients from benign clients. Then, we propose a privacy-preserving computation protocol based on homomorphic encryption to implement this detection method and perform federated aggregation while protecting the privacy of clients. Besides, a detailed theoretical analysis is given to demonstrate the privacy guarantee of the proposed protocol. Experimental results on real-world datasets show that the proposed algorithm can effectively defend against label-flipping attacks under various data distributions.

Under the Hood of Activist Fraud Campaigns: Private Information Quality, Disclosure Incentives and Stock Lending Dynamics

Under the Hood of Activist Fraud Campaigns: Private Information Quality, Disclosure Incentives and Stock Lending Dynamics

Why do audit clients voluntarily disclose the compliance and planning components of auditor provided tax services?

Criticisms of audit firms marketing and devising tax avoidance strategies for their audit clients have led to calls from regulators, investors, and proxy advisors for more detailed disclosures on the composition of tax non-audit services (NAS). Using a hand-collected dataset, we examine the determinants of audit clients’ voluntary bifurcation of total tax NAS fees into the tax planning and compliance components, and the effect of these voluntary disclosures on firm value. We find that firms with higher levels of tax NAS are more likely to provide this disclosure, as well as firms that engage in higher levels of tax avoidance and have accounting experts serve on the audit committee. Using textual analysis, we find that among voluntary tax NAS fee breakdown disclosers, firms with higher total tax and, specifically, tax planning NAS fees have longer and less similar tax NAS fee disclosures relative to the prior period disclosure. Our firm value analyses indicate higher valuations for firms providing voluntary breakdown disclosures when these disclosures reveal high tax NAS fees generally, and high tax planning NAS fees specifically. The firm valuation effects are incrementally more pronounced when voluntary tax NAS fee breakdown firms report higher tax avoidance and expert audit committee oversight. Our results suggest that the voluntary disclosure of private information regarding the nature of tax NAS is one mechanism through which firms facing scrutiny over the implications of these services can alleviate information asymmetry and stakeholder skepticism of tax NAS.

What should a robot disclose about me? A study about privacy-appropriate behaviors for social robots.

For robots to become integrated into our daily environment, they must be designed to gain sufficient trust of both users and bystanders. This is in particular important for social robots including those that assume the role of a mediator, working towards positively shaping relationships and interactions between individuals. One crucial factor influencing trust is the appropriate handling of personal information. Previous research on privacy has focused on data collection, secure storage, and abstract third-party disclosure risks. However, robot mediators may face situations where the disclosure of private information about one person to another specific person appears necessary. It is not clear if, how, and to what extent robots should share private information between people. This study presents an online investigation into appropriate robotic disclosure strategies. Using a vignette design, participants were presented with written descriptions of situations where a social robot reveals personal information about its owner to support pro-social human-human interaction. Participants were asked to choose the most appropriate robot behaviors, which differed in the level of information disclosure. We aimed to explore the effects of disclosure context, such as the relationship to the other person and the information content. The findings indicate that both the information content and relationship configurations significantly influence the perception of appropriate behavior but are not the sole determinants of disclosure-adequacy perception. The results also suggest that expected benefits of disclosure and individual general privacy attitudes serve as additional influential factors. These insights can inform the design of future mediating robots, enabling them to make more privacy-appropriate decisions which could foster trust and acceptance.