The concept of patient confidentiality is nearly as old as the practice of health professions and, has evolved and transformed over the years, from one jurisdiction to the other. Patient confidentiality can be a fundamental human right, an ethical duty or, a legal duty. The Saudi laws have evolved around its Shari’ah-based legal culture, its history and the international human right laws (IHRLs). These elements have moulded the Saudi Arabia’s unique perspective on patient confidentiality. Its confidentiality laws are found scattered in several legislations. Is the Saudi patient confidentiality law able to adequately deal with the contemporary challenges? The study reviewed the relevant Saudi laws in the light the International Humana Rights Laws. Findings suggest that there are issues bordering on the lack of quality comprehensive data protection laws, on clarity and foreseeability of the existing laws, and on the accessibility of the courts. Furthermore, the lack of a system of law reporting and stare decisis potentially gave the judges a wider latitude of discretion in interpreting the laws. Therefore, the study recommends for a comprehensive data protection law with a clear definition of “confidential information”, of data controllers and their role, and of specific safeguards against potential abuses. Others include defining legitimate purposes for using the patient’s data, and his role, and the extent to which he can control the use of his own data. Consistency in legal interpretations, and an improved law reporting system could positively enhance the overall outcome.

With a data revolution underway for some time, there is an increasing demand for formal privacy protection mechanisms that are not so destructive. Hereof microaggregation is a popular high-utility approach designed to satisfy the popular k-anonymity criteria while applying low distortion to data. However, standard performance metrics are commonly based on mean square error, which will hardly capture the utility degradation related to a specific application domain of data. In this work, we evaluate the performance of k-anonymous microaggregation in terms of the loss in classification accuracy of the machine learned models built from perturbed data. Systematic experimentation is carried out on four microaggregation algorithms that are tested over four data sets. The empirical utility of the resulting microaggregated data is assessed using the learning algorithm that obtains the highest accuracy from original data. Validation tests are performed on a test set of non perturbed data. The results confirm k-anonymous microaggregation as a high-utility privacy mechanism in this context and distortion based on mean squared error as a poor predictor of practical utility. Finally, we corroborate the beneficial effects for empirical utility of exploiting the statistical properties of data when constructing privacy preserving algorithms.

Requirements for cloud services include security and privacy. Although many security patterns, privacy patterns, and non-pattern-based knowledge have been reported, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the Cloud Security and Privacy Metamodel (CSPM). CSPM uses a consistent approach to classify and support existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

The development of the Internet of Things (IoT) has triggered a virtual wave of interconnection and intercommunication among an enormous number of universal things. This has caused an exceptional surge of colossal heterogeneous information, known as an information explosion. Until now, cloud computing has filled in as a proficient method to process and store these data. Still, it came to light that by utilizing just cloud computing, pesky issues like, the expanding requests of actual-time or speed-sensitive applications and the restrictions on system transfer speed could not be solved. Consequently, another computing platform, called fog computing has been advanced as a supplement to the cloud arrangement. Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This paper takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is outlined the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, reserving and storage, asset administration, naming, safety and safeguarding of privacy are delineated while showing how this backup and facilitate the installations and various applications. Then, numerous applications like augmented reality (AR), healthcare, gaming and brain-machine interface, vehicular computing, smart scenarios etc. are highlighted to explain the fog computing application milieu. Following that, it is shown that how, despite fog computing being a features-rich platform, it is dogged by its susceptibility to several security, privacy and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

The General Data Protection Regulation (GDPR) protects the personal data of natural persons and at the same time allows the free movement of such data within the European Union (EU). Hailed as majestic by admirers and dismissed as protectionist by critics, the Regulation is expected to have a profound impact around the world, including in the African Union (AU). For European–African consortia conducting research that may affect the privacy of African citizens, the question is ‘how to protect personal data of data subjects while at the same time ensuring a just distribution of the benefits of a global digital ecosystem?’ We use location privacy as a point of departure, because information about an individual’s location is different from other kinds of personally identifiable information. We analyse privacy at two levels, individual and cultural. Our perspective is interdisciplinary: we draw from computer science to describe three scenarios of transformation of volunteered/observed information to inferred information about a natural person and from cultural theory to distinguish four privacy cultures emerging within the EU in the wake of GDPR. We highlight recent data protection legislation in the AU and discuss factors that may accelerate or inhibit the alignment of data protection legislation in the AU with the GDPR.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

It is a challenging issue to provide a secure and conditional anonymous authentication scheme in vehicle ad hoc networks(VANETs) with low storage space and computational cost. In 2008, Lu et al. [8] proposed an conditional privacy preservation scheme called ECPP protocol. The ECPP protocol provides conditional privacy preservation to vehicles in VANETs, that is, on one hand vehicles can achieve anonymous authentication in the network, on the other hand allow to be traced and revoked if necessary. However, ECPP scheme suffers from large storage and high computational cost. In our scheme, an improved protocol based on the concept of ECPP protocol has been proposed, which uses minimal interaction steps, little storage space and less computation overhead to achieve more efficiency conditional privacy preservation(MECPP) scheme in VANETs.

Genetic testing has expanded out of the research laboratory into medical practice and the direct-to-consumer market, and rapid analysis of the resulting genotype data can now have significant impact. We present a method for summarizing personal genotypes as ‘genotype fingerprints’ that meet these needs. Genotype fingerprints can be derived from any single nucleotide polymorphism (SNP)-based assay, and remain comparable as chip designs evolve to higher marker densities. We demonstrate that they support distinguishing types of relationships among closely related individuals and closely related individuals from individuals from the same background population, as well as high-throughput identification of identical genotypes, individuals in known background populations, and de novo separation of subpopulations within a large cohort through extremely rapid comparisons. While fingerprints do not preserve anonymity, they provide a useful degree of privacy by summarizing a genotype in a way that prevents reconstruction of individual marker states. Genotype fingerprints are therefore well-suited as a format for public aggregation of genetic information to support ancestry and relatedness determination without revealing personal health risk status.

Social media and other web 2.0 tools have provided users the platform to interact and also disclose personal information not only with their friends and acquaintances, but also with relative strangers with unprecedented ease. This has enhanced the ability of people to share more about themselves, their families, and their friends through a variety of media including text, photo, and video, thus developing and sustaining social and business relationships. The purpose of the paper is to identify the factors that predict self-disclosure on social networking sites within the Ghanaian context. Data was collected from 452 students in three leading universities in Ghana and analyzed with Partial Least Square-Structural Equation Modeling. Results from the study revealed that all variables in the proposed model with the exception of interaction and perceived control were significant predictors of self-disclosure with privacy risk being the most significant predictor. In all, the model accounted for 54.6 percent of the variance in self disclosure. The implications and limitations of the current study are discussed and directions for future research proposed.

This paper presents a method for a decentralised peer-to-peer software license validation system using cryptocurrency blockchain technology to ameliorate software piracy, and to provide a mechanism for software developers to protect copyrighted works. Protecting software copyright has been an issue since the late 1970's and software license validation has been a primary method employed in an attempt to minimise software piracy and protect software copyright. The method described creates an ecosystem in which the rights and privileges of participants are observed.

Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some on-line resource, new information is added to the user profile. Exposing private data does not only reveal information about single users' preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident on \emph{social networks} where users receive suggestions based on their friends' activity. We propose an information theoretic approach to measure the differential update of the anonymity risk for time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third party services get to know about the users when a new activity is shared. We use real Facebook data to show how our model can be applied on a real world scenario.

This paper advances privacy theory through examination of online shaming, focusing in particular on persecution by internet mobs. While shaming is nothing new, the technology used for modern shaming is new and evolving, making it a revealing lens through which to analyze points of analytical friction within and between traditional conceptions of privacy. To that end, this paper first explores the narrative and structure of online shaming, identifying broad categories of shaming of vigilantism, bullying, bigotry and gossiping, which are then used throughout the paper to evaluate different angles to the privacy problems raised. Second, this paper examines shaming through three dominant debates concerning privacy - privacy’s link with dignity, the right to privacy in public places and the social dimension of privacy. Certain themes emerged from this analysis. A common feature of online shaming is public humiliation. A challenge is to differentiate between a humbling (rightly knocking someone down a peg for a social transgression) and a humiliation that is an affront to dignity (wrongly knocking someone down a peg). In addition, the privacy concern of shamed individuals is not necessarily about intrusion on seclusion or revelation of embarrassing information, but rather about the disruption in their ability to continue to participate in online spaces free from attack. The privacy interest therefore becomes more about enabling participation in social spaces, enabling connections and relationships to form, and about enabling identity-making. Public humiliation through shaming can disrupt all of these inviting closer scrutiny concerning how law can be used as an enabling rather than secluding tool.

This project designed a set of network security mechanisms for cloud applications in VANETs. At present, cloud computing is one of the government’s development priorities in the industry. We divide cloud into public, private, and hybrid clouds in this project. Vehicles or passengers can access road conditions or public transport information through the public cloud; public transportation can access current driving records and users can access relevant enterprise information on the private cloud and hybrid cloud is a combination of public and private clouds. Both cloud computing or VANETs require network and information security. At present, many researches on VANETs security only focus on message communication, and neglect information storage. Researches on cloud computing security only focus on information protection, and neglect user privacy and anonymity. This project designed a set of network and information security mechanisms in line with the requirements of Confidentiality, Authentication, Non-repudiation, Conditional Anonymity, and Conditional Untraceability. This project primarily needs to achieve the following: 1.an authentication mechanism to verify the identity of each other between the passenger and the vehicle and to verify the identity with Single Sign-On; 2. vehicle or user privacy and anonymity, which need to be able to replace the anonymous ID and related parameters for the vehicle or the user; 3. a private communication mechanism, which enables any vehicle or user to communicate privately; and 4. an information security encryption method, which can encrypt the information on a cloud server to avoid unauthorized access by internal personnel or hackers.