Biblio

Blockchain technology (BCT) has paved a way for new potentials of handling serious data privacy, integrity and security issues in healthcare. To curb the increasing challenges in healthcare industry, healthcare organizations need to apply blockchain technology to better improve patient safety and protect patients records from counterfeiting and fraud. The purpose of this research paper was to define BCT can assist in improving pharmaceutical industries in Saudi Arabia upon utilization of its application. This study adopted quantitative methods to gather the study data. Based on healthcare leaders perception and Internet connection, lack of cooperation, and economic inequality were found to be leading factors hindering the application of blockchain technology in the pharmaceutical industries, Saudi Arabia. Factors facilitating the application of blockchain technology in the pharmaceutical industries, Saudi Arabia were found as system robustness of BCT, increased data safety and decentralization, need for enhanced supply chain management and interoperability, and government laws and policies. Adopting interventions that are targeted to specific patient population medications, effective delivery systems, transit provider reimbursement far from intensity and volume of services towards value and quality was found to compromise the pre-existent challenges and real capacity in healthcare system. Although the relationship between implementation of blockchain technology and cost spending is negative in the short-term, in the long run, the relationship is positive Blockchain helps in managing multiple levels in a more secure way, reduces paper work and amplifies verification inefficiency.

Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.

With the development of various technologies, the modern industry has been promoted to a new era known as Industry 4.0. Within such paradigm, smart factories are becoming widely recognized as the fundamental concept. These systems generate and exchange vast amounts of privacy-sensitive data, which makes them attractive targets of attacks and unauthorized access. To improve privacy and security within such environments, a more decentralized approach is seen as the solution to allow their longterm growth. Currently, the blockchain technology represents one of the most suitable candidate technologies able to support distributed and secure ecosystem for Industry 4.0 while ensuring reliability, information integrity and access authorization. Blockchain based access control frameworks address encountered challenges regarding the confidentiality, traceability and notarization of access demands and procedures. However significant additional fears are raised about entities' privacy regarding access history and shared policies. In this paper, our main focus is to ensure strong privacy guarantees over the access control related procedures regarding access requester sensitive attributes and shared access control policies. The proposed scheme called PDAMF based on ring signatures adds a privacy layer for hiding sensitive attributes while keeping the verification process transparent and public. Results from a real implementation plus performance evaluation prove the proposed concept and demonstrate its feasibility.

The COVID-19 pandemic has recently emerged as a worldwide health emergency that necessitates coordinated international measures. To contain the virus's spread, governments and health organisations raced to develop vaccines that would lower Covid-19 morbidity, relieve pressure on healthcare systems, and allow economies to open. Following the COVID-19 vaccine, the vaccination certificate has been adopted to help the authorities formulate policies by controlling cross-border travelling. To address serious privacy concerns and eliminate the need for third parties to retain the trust and govern user data, in this paper, we leverage blockchain technologies in developing a secure and verifiable vaccination certificate. Our approach has the advantage of utilising a hybrid approach that implements different advanced technologies, such as the self-sovereignty concept, smart contracts and interPlanetary File System (IPFS). We rely on verifiable credentials paired with smart contracts to make decisions about who can access the system and provide on-chain verification and validation of the user and issuer DIDs. The approach was further analysed, with a focus on performance and security. Our analysis shows that our solution satisfies the security requirements for immunisation certificates.

Nowadays , cloud -based technology has been enlarged depends on the human necessities in the world. A lot of technologies is discovered that serve the people in different ways of cloud -based security and best resource allocation. Cloud-based technology is the essential factor to the resources like hardware, software for effective resource utilization . The securing applications enabled security mechanism enables the vital role for cloud -based web security through the secured password. The violation of data by the unauthorized access of users concerns many web developers and application owners . Web security enables the cloud-based password management system that illustrates the data storage and the web passwords access through the "Cloud framework". Web security, End-to-end passwords , and all the browser -based passwords could belong to the analysis of web security . The aim is to enhance system security. Thus, sensitive data are sustained with security and privacy . In this paper , the proposed Password Management via cloud-based web security gets to attain . An efficient Double Layer Password Encryption (DLPE ) algorithm to enable the secured password management system . Text -based passwords continue to be the most popular method of online user identification . They safeguard internet accounts with important assets against harmful attempts on passwords. The security of passwords is dependent on the development of strong passwords and keeping them from being stolen by intruders . The proposed DLPE algorithm perceived the double - layer encryption system as an effective security concern. When the data user accesses the user Login , the OTP generates via mail /SMS , and the original message is encrypted using public key generation. Then the text of data gets doubly encrypted through the cloud framework . The private key is used to decipher the cipher text . If the OTP gets matched , the text is to be decrypted over the text data . When double encryption happens , the detection of data flaws, malicious attacks , application hackers gets reduced and the strong password enabled double-layer encryption attained the secured data access without any malicious attackers . The data integrity , confidentiality enabled password management . The ability to manage a distributed systems policy like the Double Layer Password encryption technique enables password verification for the data used to highly secure the data or information.

Telecommunications and data are pervasive in almost each aspect of our every-day life and new concerns progressively arise as a result of stakes related to privacy and data protection [1]. Indeed, systems development becomes data-centric leading to an ecosystem where a variety of players intervene (citizens, industry, regulators) and where the policies regarding data usage and utilization are far from consensual. The new General Data Protection Regulation (GDPR) enacted by the European Commission in 2018 has introduced new provisions including principles for lawfulness, fairness, transparency, etc. thus endorsing data subjects with new rights in regards to their personal data. In this context, a growing need for approaches that conceptualize and help engineers to integrate GDPR and privacy provisions at design time becomes paramount. This paper presents a comprehensive approach to support different phases of the design process with special attention to the integration of privacy and data protection principles. Among others, it is a generic model-based approach that can be specialized according to the specifics of different application domains.

A trusted execution environment (TEE) such as Intel Software Guard Extension (SGX) runs attestation to prove to a data owner the integrity of the initial state of an enclave, including the program to operate on her data. For this purpose, the data-processing program is supposed to be open to the owner or a trusted third party, so its functionality can be evaluated before trust being established. In the real world, however, increasingly there are application scenarios in which the program itself needs to be protected (e.g., proprietary algorithm). So its compliance with privacy policies as expected by the data owner should be verified without exposing its code.To this end, this paper presents DEFLECTION, a new model for TEE-based delegated and flexible in-enclave code verification. Given that the conventional solutions do not work well under the resource-limited and TCB-frugal TEE, we come up with a new design inspired by Proof-Carrying Code. Our design strategically moves most of the workload to the code generator, which is responsible for producing easy-to-check code, while keeping the consumer simple. Also, the whole consumer can be made public and verified through a conventional attestation. We implemented this model on Intel SGX and demonstrate that it introduces a very small part of TCB. We also thoroughly evaluated its performance on micro-and macro-benchmarks and real-world applications, showing that the design only incurs a small overhead when enforcing several categories of security policies.

We perform the first post EU General Data Protection Regulation (GDPR) usability study of privacy policies for mobile apps. For our analysis, we collect a dataset of historical (prior to GDPR implementation in May 2018) and contemporary privacy policies in different categories. In contrast to the common belief, that after the GDPR most of the privacy policies are easier to understand, our analysis shows that this is not so.

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired.In this paper, we propose to leverage data augmentation to defeat reconstruction attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract any useful information from the corresponding gradients. We design a novel search method to automatically discover qualified policies. We adopt two new metrics to quantify the impacts of transformations on data privacy and model usability, which can significantly accelerate the search speed. Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.

Demand-side management (DSM) plays a significant function in the smart distribution system to make informed decisions from both the consumer and supplier side with regards to energy consumption to redesign the load profile and to decrease the peak load demand. This study extensively reviews the demand-side management (DSM) strategies along with both demand response and energy efficiency policies. The major objective of this paper is to enumerate the relevant features responsible to strengthen the DSM effectively, particularly for residential energy demand and the limits to energy indicators. Secondly, the large untapped and hidden potential and the associated barriers to energy efficiency enhancement are focused and surveyed for formulating a better number of potential policy responses. This further explores the portfolio approach with bundled strategies to reflect on the power market through enhancing the strength of individual residential measures through complementary policies to reduce the weaknesses. This concludes at last with the findings of possible holistic measures related to various approaches and attributes findings that reinforce the DSM strategies to enhance energy management and cost-effectiveness. Apart from that the architecture, formulation of optimization problems, and various approaches are presented to help the readers to develop research in this direction to maximize the total system peak demand, overall load factor, and utility revenue with the minimized customer electric bill.

Blockchain-based cryptocurrencies offer an appealing alternative to Fiat currencies, due to their decentralized and borderless nature. However the decentralized settings make the authentication process more challenging: Standard cryptographic methods often rely on the ability of users to reliably store a (large) secret information. What happens if one user's key is lost or stolen? Blockchain systems lack of fallback mechanisms that allow one to recover from such an event, whereas the traditional banking system has developed and deploys quite effective solutions. In this work, we develop new cryptographic techniques to integrate security policies (developed in the traditional banking domain) in the blockchain settings. We propose a system where a smart contract is given the custody of the user's funds and has the ability to invoke a two-factor authentication (2FA) procedure in case of an exceptional event (e.g., a particularly large transaction or a key recovery request). To enable this, the owner of the account secret-shares the answers of some security questions among a committee of users. When the 2FA mechanism is triggered, the committee members can provide the smart contract with enough information to check whether an attempt was successful, and nothing more. We then design a protocol that securely and efficiently implements such a functionality: The protocol is round-optimal, is robust to the corruption of a subset of committee members, supports low-entropy secrets, and is concretely efficient. As a stepping stone towards the design of this protocol, we introduce a new threshold homomorphic encryption scheme for linear predicates from bilinear maps, which might be of independent interest. To substantiate the practicality of our approach, we implement the above protocol as a smart contract in Ethereum and show that it can be used today as an additional safeguard for suspicious transactions, at minimal added cost. We also implement a second scheme where the smart contract additionally requests a signature from a physical hardware token, whose verification key is registered upfront by the owner of the funds. We show how to integrate the widely used universal two-factor authentication (U2F) tokens in blockchain environments, thus enabling the deployment of our system with available hardware.

Commodity I/O hardware often fails to separate I/O transfers of isolated OS and applications code. Even when using the best I/O hardware, commodity systems sometimes trade off separation assurance for increased performance. Remarkably, device firmware need not be malicious. Instead, any malicious driver, even if isolated in its own execution domain, can manipulate its device to breach I/O separation. To prevent such vulnerabilities with high assurance, a formal I/O separation model and its use in automatic generation of secure I/O kernel code is necessary.This paper presents a formal I/O separation model, which defines a separation policy based on authorization of I/O transfers and is hardware agnostic. The model, its refinement, and instantiation in the Wimpy kernel design, are formally specified and verified in Dafny. We then specify the kernel implementation and automatically generate verified-correct assembly code that enforces the I/O separation policies. Our formal modeling enables the discovery of heretofore unknown design and implementation vulnerabilities of the original Wimpy kernel. Finally, we outline how the model can be applied to other I/O kernels and conclude with the key lessons learned.

Observational non-interference (ONI) is a generic information-flow policy for side-channel leakage. Informally, a program is ONI-secure if observing program leakage during execution does not reveal any information about secrets. Formally, ONI is parametrized by a leakage function l, and different instances of ONI can be recovered through different instantiations of l. One popular instance of ONI is the cryptographic constant-time (CCT) policy, which is widely used in cryptographic libraries to protect against timing and cache attacks. Informally, a program is CCT-secure if it does not branch on secrets and does not perform secret-dependent memory accesses. Another instance of ONI is the constant-resource (CR) policy, a relaxation of the CCT policy which is used in Amazon's s2n implementation of TLS and in several other security applications. Informally, a program is CR-secure if its cost (modelled by a tick operator over an arbitrary semi-group) does not depend on secrets.In this paper, we consider the problem of preserving ONI by compilation. Prior work on the preservation of the CCT policy develops proof techniques for showing that main compiler optimisations preserve the CCT policy. However, these proof techniques critically rely on the fact that the semi-group used for modelling leakage satisfies the property: l1+ l1' = l2+l2'$\Rightarrow$l1=l2$\wedge$ l1' = l2' Unfortunately, this non-cancelling property fails for the CR policy, because its underlying semi-group is ($\backslash$mathbbN, +) and it is currently not known how to extend existing techniques to policies that do not satisfy non-cancellation.We propose a methodology for proving the preservation of the CR policy during a program transformation. We present an implementation of some elementary compiler passes, and apply the methodology to prove the preservation of these passes. Our results have been mechanically verified using the Coq proof assistant.

In recent years, the commodities industry has grown rapidly under the stimulus of domestic demand and the expansion of cross-border trade. It has also been combined with the rapid development of e-commerce technology in the same period to form a flexible and efficient e-commerce system for bulk commodities. However, the hasty combination of both has inspired a lack of effective regulatory measures in the bulk industry, leading to constant industry chaos. Among them, the problem of lagging evidence in regulatory platforms is particularly prominent. Based on this, we design a blockchain-based evidential and secure bulk-commodity supervisory system (abbr. BeBus). Setting different privacy protection policies for each participant in the system, the solution ensures effective forensics and tamper-proof evidence to meet the needs of the bulk business scenario.

Internet of Thing (IoT) devices are being widely used in smart homes and organizations. An IoT device has some intended purposes, but may also have hidden functionalities. Typically, the device is installed in a home or an organization and the network traffic associated with the device is captured and analyzed to infer high-level functionality to the extent possible. However, such analysis is dynamic in nature, and requires the installation of the device and access to network data which is often hard to get for privacy and confidentiality reasons. We propose an alternative static approach which can infer the functionality of a device from vendor materials using Natural Language Processing (NLP) techniques. Information about IoT device functionality can be used in various applications, one of which is ensuring security in a smart home. We demonstrate how security policies associated with device functionality in a smart home can be formally represented using the NIST Next Generation Access Control (NGAC) model and automatically analyzed using Alloy, which is a formal verification tool. This will provide assurance to the consumer that these devices will be compliant to the home or organizational policy even before they have been purchased.

Since the personal data protection law is on the way of many countries, how to use data mining method to secure sensitive information has become a challenge for enterprises. To make sure every employee follows company's data protection strategy, it may take lots of time and cost to seek and scan thousands of folders and files in user equipment, ensuring that the file contents meet IT security policies. Hence, this paper proposed a lightweight, pattern-based detection system, PIDS, which is expecting to enable an affordable data leakage prevention with essential cost and high efficiency in small business enterprise. For verification and evaluation, PIDS has been deployed on more than 100,000 PCs of collaboration enterprises, and the feedback shows that the system is able to approach its original design functionality for finding violated or sensitive contents efficiently.

The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizing communication time. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially by considering either the communication rounds or per-round latency, while the total communication time is determined by both metrics. To close this gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the communication time through cross-disciplinary effort involving both learning theory for convergence analysis and communication theory for per-round latency analysis. Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.

Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and sends it back to the UEs. However, since communication usually occurs through a limited spectrum, only a portion of the UEs can update their parameters upon each global aggregation. As such, new scheduling algorithms have to be engineered to facilitate the full implementation of FL. In this paper, based on a metric termed the age of update (AoU), we propose a scheduling policy by jointly accounting for the staleness of the received parameters and the instantaneous channel qualities to improve the running efficiency of FL. The proposed algorithm has low complexity and its effectiveness is demonstrated by Monte Carlo simulations.

In recent years, user's privacy has become an important aspect in the development of Internet of Things (IoT) devices. However, there has been comparatively little research so far that aims to understanding user's privacy in connection with IoT. Many users are worried about protecting their personal information, which may be gathered by IoT devices. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value.

Smart grid employs intelligent transmission and distribution networks for effective and reliable delivery of electricity. It uses fine-grained electrical measurements to attain optimized reliability and stability by sharing these measurements among different entities of energy management systems of the grid. There are many stakeholders like users, phasor measurement units (PMU), and other entities, with changing requirements involved in the sharing of the data. Therefore, data security plays a vital role in the correct functioning of a power grid network. In this paper, we propose an attribute-based encryption (ABE) for secure data sharing in Smart Grid architectures as ABE enables efficient and secure access control. Also, the access policy is obfuscated to preserve privacy. We use Linear Secret Sharing (LSS) Scheme for supporting any monotone access structures, thereby enhancing the expressiveness of access policies. Finally, we also analyze the security, access policy privacy and collusion resistance properties along with efficiency analysis of our cryptosystem.

The Internet of Things (IoT) has been one of the biggest revelations of the last decade. These cyber-physical systems seamlessly integrate and improve the activities in our daily lives. Hence, creating a wide application for it in several domains, such as smart buildings and cities. However, the integration of IoT also comes with privacy challenges. The privacy challenges result from the ability of these devices to pervasively collect personal data about individuals through sensors in ways that could be unknown to them. A number of research efforts have evaluated privacy policy awareness and enforcement as key components for addressing these privacy challenges. This paper provides a framework for understanding contextualized privacy policy within the IoT domain. This will enable IoT privacy researchers to better understand IoT privacy policies and their modeling.

The ability to specify various policies with different overriding criteria allows for complex sets of sharing policies. This is particularly useful in situations in which data privacy depends on various properties of the data, and complex policies are needed to express the conditions under which data is protected. However, if overriding policy decisions constrain the affected data, decisions from overridden policies should not be suppressed completely, because they can still apply to subsets of the affected data. This article describes how a privacy policy framework can be extended with a mechanism to partially override decisions based on specified constraints. Our solution automatically generates complementary sets of decisions for both the overridden and the complementary, non-overridden subsets of the data, and thus, provides a means to specify a complex policies tailored to specific properties of the protected data.

We present our journey in constructing the first integrated data warehouse for Philippine transportation research in the hopes of developing a Transportation Decision Support System for impact studies and policy making. We share how we collected data from diverse sources, processed them into a homogeneous format and applied them to our multimodal platform. We also list the challenges we encountered, including bureaucratic delays, data privacy concerns, lack of software, and overlapping datasets. The data warehouse shall serve as a public resource for researchers and professionals, and for government officials to make better-informed policies. The warehouse will also function within our multi-modal platform for measurement, modelling, and visualization of road transportation. This work is our contribution to improve the transportation situation in the Philippines, both in the local and national levels, to boost our economy and overall quality of life.

Since GDPR was introduced, there is a reinforcement of the fact that users must give their consent before their personal data can be managed by any website. However, many studies have demonstrated that users often skip these policies and click the "I agree" button to continue browsing, being unaware of what the consent they gave was about, hence defeating the purpose of GDPR. This paper investigates if different ways of presenting users the privacy policy can change this behaviour and can lead to an increased awareness of the user in relation to what the user agrees with. Three different types of policies were used in the study: a full-text policy, a so-called usable policy, and a video-based policy. Results demonstrated that the type of policy has a direct influence on the user awareness and user satisfaction. The two alternatives to the text-based policy lead to a significant increase of user awareness in relation to the content of the policy and to a significant increase in the user satisfaction in relation to the usability of the policy.

In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users' information for revenue generation through targeted advertising. The SNP can present ads to proper users efficiently. Therefore, an advertiser is more willing to pay for targeted advertising. However, the over-exploitation of users' information would invade users' privacy, which would negatively impact users' social activeness. Motivated by this, we study the optimal privacy policy of the SNP with targeted advertising business. We characterize the privacy policy in terms of the fraction of users' information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP as a three-stage Stackelberg game. By carefully leveraging supermodularity property, we reveal from the equilibrium analysis that higher information exploitation will discourage users from exhibiting information, lowering the overall amount of exploited information and harming advertising revenue. We further characterize the optimal privacy policy based on the connection between users' information levels and privacy policy. Numerical results reveal some useful insights that the optimal policy can well balance the users' trade-off between social benefit and privacy loss.