Privacy Analysis Research Articles

Analisis Privasi Dan Publikasi Postingan Media Sosial Di Kalangan Orang Muda Menurut Interaksionisme Simbolik Herbert Blumer

Social media is a popular tool for young people. Half the lives of young people are poured into social media. This is evidenced by statistical data that states that young people can spend a lot of time just to access social media. All kinds of affairs have shifted from conventional means to computerization. With the emergence of various kinds of limelight that exist on social media, young people are increasingly immersed in trends both positively and negatively. There are two general trends that young people make in social media, namely regarding privacy and the publication of posts on social media. This writing uses qualitative research methods using data collection techniques through interviews and documentation studies. The purpose of this study is to know the form of the phenomenon of publication and privacy of posts on social media for young people, and also to know the analysis of privacy and publication of social media posts among young people according to Herbert Blumer's Symbolic Interactionism. This type of research uses qualitative research methods using data collection techniques through literature studies to strengthen research arguments and interviews with five resource persons from young people with interview guide instruments and recording tools. The results of this study show strong reasons for choosing privatization, namely avoiding the curiosity of others, not everything must be published, knowing the limits to minimize crime seeds from irresponsible parties and preventing crimes such as account hacking, taking photos from social media for fraud.

Securing Distributed SGD Against Gradient Leakage Threats

This paper presents a holistic approach to gradient leakage resilient distributed Stochastic Gradient Descent (SGD). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , we analyze two types of strategies for privacy-enhanced federated learning: (i) gradient pruning with random selection or low-rank filtering and (ii) gradient perturbation with additive random noise or differential privacy noise. We analyze the inherent limitations of these approaches and their underlying impact on privacy guarantee, model accuracy, and attack resilience. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Next</i> , we present a gradient leakage resilient approach to securing distributed SGD in federated learning, with differential privacy controlled noise as the tool. Unlike conventional methods with the per-client federated noise injection and fixed noise parameter strategy, our approach keeps track of the trend of per-example gradient updates. It makes adaptive noise injection closely aligned throughout the federated model training. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , we provide an empirical privacy analysis on the privacy guarantee, model utility, and attack resilience of the proposed approach. Extensive evaluation using five benchmark datasets demonstrates that our gradient leakage resilient approach can outperform the state-of-the-art methods with competitive accuracy performance, strong differential privacy guarantee, and high resilience against gradient leakage attacks. The code associated with this paper can be found: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/git-disl/Fed-alphaCDP</uri> .

Efficient Bi-objective SQL Optimization for Enclaved Cloud Databases with Differentially Private Padding

Hardware-enabled enclaves have been applied to efficiently enforce data security and privacy protection in cloud database services. Such enclaved systems, however, are reported to suffer from I/O-size (also referred to as communication-volume)-based side-channel attacks. Albeit differentially private padding has been exploited to defend against these attacks as a principle method, it introduces a challenging bi-objective parametric query optimization (BPQO) problem and current solutions are still not satisfactory. Concretely, the goal in BPQO is to find a Pareto-optimal plan that makes a tradeoff between query performance and privacy loss; existing solutions are subjected to poor computational efficiency and high cloud resource waste. In this article, we propose a two-phase optimization algorithm called TPOA to solve the BPQO problem. TPOA incorporates two novel ideas: divide-and-conquer to separately handle parameters according to their types in optimization for dimensionality reduction; on-demand-optimization to progressively build a set of necessary Pareto-optimal plans instead of seeking a complete set for saving resources. Besides, we introduce an acceleration mechanism in TPOA to improve its efficiency, which prunes the non-optimal candidate plans in advance. We theoretically prove the correctness of TPOA, numerically analyze its complexity, and formally give an end-to-end privacy analysis. Through a comprehensive evaluation on its efficiency by running baseline algorithms over synthetic and test-bed benchmarks, we can conclude that TPOA outperforms all benchmarked methods with an overall efficiency improvement of roughly two orders of magnitude; moreover, the acceleration mechanism speeds up TPOA by 10-200×.

Tool-supported method for privacy analysis of a business process model

The shift to data-driven decision-making brings opportunities to organisations. However, reliance on sensitive data about identifiable persons poses the obligation to cope with data privacy management with respect to the local legislation. As the privacy protection regulation (i.e. GDPR) in the EU has gained its power relatively recently, no established procedures or frameworks guide privacy analysis and assurance. The paper presents a tool-supported method for privacy analysis of a business process model. The proposed method aims to support the elicitation of requirements to the information system to comply with GDPR. Additionally, the method supports the selection of technical measures for privacy assurance based on their effectiveness in the context of a business process. The method’s usability is validated by the experimental application of the method to a ride fulfilment process in a ride-hailing company enabled by an autonomous driving system.

Security and privacy analysis based on Internet of Things in the fourth industrial generation (Industry 4.0)

Security and privacy analysis based on Internet of Things in the fourth industrial generation (Industry 4.0)

Privacy Implications of Contacting the At-Risk Relatives of Patients with Medically Actionable Genetic Predisposition, with Patient Consent: A Hypothetical Australian Case Study.

Genetic risk information has relevance for patients' blood relatives. However, cascade testing uptake in at-risk families is <50%. International research supports direct notification of at-risk relatives by health professionals (HPs), with patient consent. However, HPs express concerns about the privacy implications of this practice. Our privacy analysis, grounded in a clinically relevant hypothetical scenario, considers the types of personal information involved in direct notification of at-risk relatives and the application of Australian privacy regulations. It finds that collecting relatives' contact details, and using those details (with patient consent) to notify relatives of possible genetic risk, does not breach Australian privacy law, providing that HPs adhere to regulatory requirements. It finds the purported "right to know" does not prevent disclosure of genetic information to at-risk relatives. Finally, the analysis confirms that the discretion available to HPs does not equate to a positive duty to warn at-risk relatives. Thus, direct notification of a patient's at-risk relatives regarding medically actionable genetic information, with patient consent, is not a breach of Australian privacy regulations, providing it is conducted in accordance with the applicable principles set out. Clinical services should consider offering this service to patients where appropriate. National guidelines would assist with the clarification of the discretion for HPs.

Compressed Sensing-Based Privacy Preserving in Labeled Dynamic Social Networks

With the rapid development of social networks, privacy- preserving is an important issue. Nowadays, privacy-preserving mainly involves static social networks. In fact, social networks are dynamic. The sequential release of social networks will lead to privacy leakage. Many studies have employed <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-anonymity to protect users&#x0027; privacy, but because <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> affects data utility in social networks, a smaller <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> cannot provide enough privacy preserving, whereas a larger <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> will lead to a significant loss of data utility. This article proposes a new privacy-preserving approach based on compressed sensing (CS) to protect privacy in the labeled dynamic social networks. The scheme first uses the CS to compress the updated node information at each time point. Then, in the form of label grouping, the node link relationship is randomly deleted/changed to blur the node degrees to protect privacy. Finally, the reconstruction algorithm and Gaussian measurement matrix are combined to ensure operating efficiency while balancing data privacy and data utility. The simulation results show that our scheme retains data utility better than <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula>-diversity and <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-anonymity. Furthermore, according to privacy analysis, the scheme can also protect link relationship privacy and label privacy at the same time, and prevent background knowledge attacks.

Decentralized federated learning through proxy model sharing

Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator’s data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant’s privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.

Automating Internet of Things Network Traffic Collection with Robotic Arm Interactions

Consumer Internet of things research often involves collecting network traffic sent or received by IoT devices. These data are typically collected via crowdsourcing or while researchers manually interact with IoT devices in a laboratory setting. However, manual interactions and crowdsourcing are often tedious, expensive, inaccurate, or do not provide comprehensive coverage of possible IoT device behaviors. We present a new method for generating IoT network traffic using a robotic arm to automate user interactions with devices. This eliminates manual button pressing and enables permutation-based interaction sequences that rigorously explore the range of possible device behaviors. We test this approach with an Arduinocontrolled robotic arm, a smart speaker, and a smart thermostat, using machine learning to demonstrate that collected network traffic contains information about device interactions that could be useful for network, security, or privacy analyses. We also provide source code and documentation allowing researchers to easily automate IoT device interactions and network traffic collection in future studies.

THE DETERMINANTS OF CONSUMERS' ONLINE SHOPPING BEHAVIOUR: AN EMPIRICAL ASSESSMENT

The study focuses on the effect of four primary characteristics derived from the literature, including trust, product variety, convenience, and privacy, so as to identify how customer purchasing behavior reflects online shopping trends. The researcher used quantitative research approach to documents responses of respondents. Students attending Thal university &amp; Sargodha university are mostly those engaged in business programme at respective institutions, such as the Department of Business Administration and Noon Business school was accessed to collect the data through questioner. The researchers use structural equational model to test hypothesis of current research, analyze the results and reach the desired conclusion and thus recommending some suggestions. According to the findings of statistical analysis of data, customers trust, product variety, convenience and privacy has statistically significant relationship with the online consumer behaviour. This conclusion is vital for the marketers to facilitate the usage of online shopping platforms in order to save more time and attract more people to buy items online.

Privacy-Preserving Dynamic Average Consensus via Random Number Perturbation

This brief focuses on the study of privacy preservation of dynamic average consensus (DAC) in multi-agent networks. A privacy-preserving DAC (PP-DAC) algorithm is proposed based on a carefully designed random number perturbation mechanism. The PP-DAC algorithm is able to protect agents from the leakage of sensitive information without compromising their tracking accuracy. Furthermore, the privacy analysis for different scenarios is given to show that the PP-DAC algorithm works well unless all neighbors of the target agent collude with each other to attack this agent. Also, some numerical simulations are given to illustrate the validity of the proposed algorithm.

Event-Trigger-Based Finite-Time Privacy-Preserving Formation Control for Multi-UAV System

Privacy-preserving has been crucial technique of multi-UAV systems, including cooperative detection, cooperative penetration and strike. Unprocessed interactive information poses a serious privacy threat to UAV swarm collaborative tasks. Considering not only privacy-preserving but also bandwidth constraints and the convergence performance of multi-UAV systems, this paper comprehensively proposes an original event-triggered-based finite-time privacy-preserving formation control scheme to resolve these three factors. Firstly, this paper adopted a local, deterministic, time-varying output mapping function for a privacy mask, which encodes the internal states of the UAV prior to its public transmission, and the initial true value of each UAV’s states is kept indecipherable for honest-but-curious UAVs and other malicious eavesdropping attackers. Then, considering the limited communication bandwidth and channels, we employed a distributed event-triggered strategy and deduced the triggering condition for consensus-based formation control, which effectively reduces the excessive consumption of communication and computational resources in contrast to time-triggered strategy. In terms of the convergence performance of the UAVs, finite-time stability theory was introduced to make the system reach the desired formation in finite time and obtain a settling time related to the initial state. Compared with the existing literature, this paper systematically took into account the above three factors for multi-UAV systems and provides a convergence analysis and a privacy analysis in detail. Finally, the effectiveness of the finite-time privacy-preserving protocol based on an event-triggered strategy was demonstrated by numerical simulation examples and comparative experiments. The proposed method achieves the formation control under privacy-preserving, improves the convergence rate and reduces the frequency of controller updates and information transmission.

Using Blockchain to Achieve Decentralized Privacy in IoT Healthcare

With the advent of the Internet of Things (IoT), e-health has become one of the main topics of research. Due to the sensitivity of patient information, patient privacy seems challenging. Nowadays, patient data is usually stored in the cloud in healthcare programs, making it difficult for users to have enough control over their data. The recent increment in announced cases of security and surveillance breaches compromising patients' privacy call into question the conventional model, in which third-parties gather and control immense amounts of patients' Healthcare data. In this work, we try to resolve the issues mentioned above by using blockchain technology. We propose a blockchain-based protocol suitable for ehealth applications that does not require trust in a third party and provides an efficient privacypreserving access control mechanism. Transactions in our proposed system, unlike Bitcoin, are not entirely financial, and we do not use conventional methods for consensus operations in blockchain like Proof of Work (PoW). It is not suitable for IoT applications because IoT devices have resourcesconstraints. Usage of appropriate consensus method helps us to increase network security and efficiency, as well as reducing network cost, i.e., bandwidth and processor usage. Finally, we provide security and privacy analysis of our proposed protocol.

Between Concealment and Disclosure: Approaches to the History of Privacy in Knowledge-Making

Between Concealment and Disclosure: Approaches to the History of Privacy in Knowledge-Making

Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges

The recent proliferation of ubiquitous computing technologies has led to the emergence of urban computing that aims to provide intelligent services to inhabitants of smart cities. Urban computing deals with enormous amounts of data collected from sensors and other sources in a smart city. In this article, we investigated and highlighted the role of urban computing in sustainable smart cities. In addition, a taxonomy was conceived that categorized the existing studies based on urban data, approaches, applications, enabling technologies, and implications. In this context, recent developments were elucidated. To cope with the engendered challenges of smart cities, we outlined some crucial use cases of urban computing. Furthermore, prominent use cases of urban computing in sustainable smart cities (e.g., planning in smart cities, the environment in smart cities, energy consumption in smart cities, transportation in smart cities, government policy in smart cities, and business processes in smart cities) for smart urbanization were also elaborated. Finally, several research challenges (such as cognitive cybersecurity, air quality, the data sparsity problem, data movement, 5G technologies, scaling via the analysis and harvesting of energy, and knowledge versus privacy) and their possible solutions in a new perspective were discussed explicitly.

IoMT: A COVID-19 Healthcare System driven by Federated Learning and Blockchain.

Internet of medical things (IoMT) has made it possible to collect applications and medical devices for improving healthcare information technology. Today, limitations in technology enable COVID-19 call centers to restrict the number of calls per day. To this end, the unprecedented virality of COVID-19 makes call centers to be likely overstressed. Thus, people who are tested for COVID-19 virus may not get adequate guidance to manage and minimize both its risk and transmission. In addition, lack of patients privacy has restricted the sharing of COVID-19 data among health institutions. To resolve the above mentioned limitations, this paper proposes privacy infrastructure based on federated learning and blockchain technology. The proposed infrastructure has the potentials to enhance public communication and deliver alternative methods to disseminate COVID-19 information. Also, the proposed infrastructure can effectively resolve the issue of large data silos and provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security related attacks.

ZPA: A Smart Home Privacy Analysis System Based on ZigBee Encrypted Traffic

Currently, the ZigBee protocol is widely used in smart homes and provides convenience to people. However, smart home devices often carry a large amount of real physical world information, which may result in information leakage problems. In this paper, to reveal the privacy security issues existing in ZigBee-based smart home networks, we design a smart home privacy analysis system based on ZigBee-encrypted traffic, called ZPA. ZPA can extract ZigBee data features based on the device’s operating mode and time window and use state-of-the-art machine-learning models to identify the type and status of smart home devices that could leak users’ private information. Through the analysis of 20 different devices from 5 manufacturers, the results show that even if the ZigBee traffic is protected by encryption, the accuracy of the proposed method in device type identification and state inference can reach approximately 93% and 98%, respectively. The types and statuses of devices in smart homes will reveal the user’s activity information to a certain extent. The privacy security of ZigBee-based smart devices still needs to be further strengthened.

Privacy-preserving association rule mining via multi-key fully homomorphic encryption

Association rule mining is an efficient method to mine the association relationships between different items from large transaction databases, but is vulnerable to privacy leakage as operates over users’ sensitive data directly. Privacy-preserving association rule mining has emerged to protect users’ privacy during rule mining. Unfortunately, existing privacy-preserving association rule mining schemes suffer from high overhead, fail to support multiple users, and are challenging to prevent collusion attacks between twin-server. To this end, in this paper, we propose a privacy-preserving association rule mining solution via multi-key fully homomorphic encryption over the torus (MKTFHE), which efficiently supports multiple users through a single server only. Specifically, we first construct some multi-key homomorphic gates based on MKTFHE. Then, we designed a series of privacy-preserving computational protocols based on multi-key homomorphic gates. Finally, we build a privacy-preserving association rule mining system with a single cloud server to support multiple users. Moreover, privacy analysis and performance evaluation demonstrate our proposal is efficient and feasible. In contrast to existing solutions, the proposed scheme outperforms encryption and communication, saving approximately 8.5% running time.

Total variation distance privacy: Accurately measuring inference attacks and improving utility

Differential privacy (DP) is a general approach to defend against inference attacks, but hard to balance the privacy-utility trade-off for some complex data analysis tasks. To improve the utility of data analysis, a weaker privacy definition with a more accurate estimate of privacy risk may be a straightforward and effective solution. Total variation distance (TVD) privacy is an appropriate tool for this issue, but it has not been adequately studied. In this paper, we systematically study TVD privacy and explore its applications. We provide a comprehensive theoretical analysis of TVD privacy and demonstrate its advantage in measuring privacy risks with the example of membership inference attacks. Our work indicates that TVD privacy is a helpful tool in estimating privacy risks and has the potential to be widely used as a general privacy definition.

Policy Suggestions for Transplantation of Organs in India: Use of Blockchain Technology to Manage Organ Donation

Abstract One of the most honorable acts of humanity is the donation of an organ or organs to save the lives of patients with severe diseases that necessitate organ replacement. Organs can be donated by people who are alive as well as dead. India has seen a noteworthy upsurge in organ donation following the death of the brain in recent years. There are various legal, clinical, ethical, and technical constraints implicated in the current organ donation and transplantation systems in terms of registering recipients, matching donors with recipients, removing organs, delivering organs, and transplanting organs. An end-to-end system is essential to ensure an impartial and competent organ donation and transplantation process. Use of blockchain technology in order to better manage organ donation and allocation can be one of the most efficient ways to remedy this situation. The article proposes blockchain-based solution to ensure complete decentralization, security, traceability, privacy, and trustworthiness in the donation and transplantation of organs. By performing confidentiality, safety, and privacy analyses and linking our projected resolution with the prevailing one, we evaluate the performance of the suggested solution. Adopting the above-mentioned measures will lead to the creation of block chain platforms or hyper ledger fabric frameworks, streamlining organ donation and allocation processes, which will benefit India as it moves toward the digital future in health care.