Electronic workplace surveillance and employee privacy : a comparative analysis of privacy protection in Australia and the United States

More than a century ago in their definitive work “The Right to Privacy” Samuel D. Warren and Louis D. Brandeis highlighted the challenges posed to individual privacy by advancing technology. Today’s workplace is characterised by its reliance on computer technology, particularly the use of email and the Internet to perform critical business functions. Increasingly these and other workplace activities are the focus of monitoring by employers. There is little formal regulation of electronic monitoring in Australian or United States workplaces. Without reasonable limits or controls, this has the potential to adversely affect employees’ privacy rights. Australia has a history of legislating to protect privacy rights, whereas the United States has relied on a combination of constitutional guarantees, federal and state statutes, and the common law. This thesis examines a number of existing and proposed statutory and other workplace privacy laws in Australia and the United States. The analysis demonstrates that existing measures fail to adequately regulate monitoring or provide employees with suitable remedies where unjustifiable intrusions occur. The thesis ultimately supports the view that enacting uniform legislation at the national level provides a more effective and comprehensive solution for both employers and employees. Chapter One provides a general introduction and briefly discusses issues relevant to electronic monitoring in the workplace. Chapter Two contains an overview of privacy law as it relates to electronic monitoring in Australian and United States workplaces. In Chapter Three there is an examination of the complaint process and remedies available to a hypothetical employee (Mary) who is concerned about protecting her privacy rights at work. Chapter Four provides an analysis of the major themes emerging from the research, and also discusses the draft national uniform legislation. Chapter Five details the proposed legislation in the form of the Workplace Surveillance and Monitoring Act, and Chapter Six contains the conclusion.

An annotation-free method for evaluating privacy protection techniques in videos

While several privacy protection techniques are pre- sented in the literature, they are not complemented with an established objective evaluation method for their assess- ment and comparison. This paper proposes an annotation- free evaluation method that assesses the two key aspects of privacy protection that are privacy and utility. Unlike some existing methods, the proposed method does not rely on the use of subjective judgements and does not assume a spe- cific target type in the image data. The privacy aspect is quantified as an appearance similarity and the utility aspect is measured as a structural similarity between the original raw image data and the privacy-protected image data. We performed an extensive experimentation using six challeng- ing datasets (including two new ones) to demonstrate the effectiveness of the evaluation method by providing a per- formance comparison of four state-of-the-art privacy pro- tection techniques.

Routing and privacy protection in human associated delay tolerant networks

This thesis proposes Human Associated Delay Tolerant Networks, where data communications among mobile nodes are determined by human social behaviours. Three models are proposed to handle the social attributes effect on data forwarding, the time impact on nodes’ movement and the privacy protection issue when social attributes are introduced.

Privacy as human flourishing: Could a shift towards virtue ethics strengthen privacy protection in the age of Big Data?

Privacy is commonly seen as an instrumental value in relation to negative freedom, human dignity and personal autonomy. Article 8 ECHR, protecting the right to privacy, was originally coined as a doctrine protecting the negative freedom of citizens in vertical relations, that is between citizen and state. Over the years, the Court has extended privacy protection to horizontal relations and has gradually accepted that individual autonomy is an equally important value underlying the right to privacy. However, in most of the recent cases regarding Article 8 ECHR, the Court goes beyond the protection of negative freedom and individual autonomy and instead focuses self-expression, personal development and human flourishing. Accepting this virtue ethical notion, in addition to the traditional Kantian focus on individual autonomy and human dignity, as a core value of Article 8 ECHR may prove vital for the protection of privacy in the age of Big Data.

Time-series pattern based effective noise generation for privacy protection on cloud

Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service providers may exist in these cloud environments, and some of them may record service data from a customer and then collectively deduce the customer's private information without permission. Therefore, from the perspective of cloud customers, it is essential to take certain technical actions to protect their privacy at client side. Noise obfuscation is an effective approach in this regard by utilizing noise data. For instance, noise service requests can be generated and injected into real customer service requests so that malicious service providers would not be able to distinguish which requests are real ones if these requests' occurrence probabilities are about the same, and consequently related customer privacy can be protected. Currently, existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case, the probability fluctuation could not be concealed by existing noise generation strategies, and it is a serious risk for the customer's privacy. To address this probability fluctuation privacy risk, we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First, we analyze this privacy risk and present a novel cluster based algorithm to generate time intervals dynamically. Then, based on these time intervals, we investigate corresponding probability fluctuations and propose a novel time-series pattern based forecasting algorithm. Lastly, based on the forecasting algorithm, our novel noise generation strategy can be presented to withstand the probability fluctuation privacy risk. The simulation evaluation demonstrates that our strategy can significantly improve the effectiveness of such cloud privacy protection to withstand the probability fluctuation privacy risk.

Privacy Region Protection for H.264/AVC with Enhanced Scrambling Effect and a Low Bitrate Overhead

While video surveillance systems have become ubiquitous in our daily lives, they have introduced concerns over privacy invasion. Recent research to address these privacy issues includes a focus on privacy region protection, whereby existing video scrambling techniques are applied to specific regions of interest (ROI) in a video while the background is left unchanged. Most previous work in this area has only focussed on encrypting the sign bits of nonzero coefficients in the privacy region, which produces a relatively weak scrambling effect. In this paper, to enhance the scrambling effect for privacy protection, it is proposed to encrypt the intra prediction modes (IPM) in addition to the sign bits of nonzero coefficients (SNC) within the privacy region. A major issue with utilising encryption of IPM is that drift error is introduced outside the region of interest. Therefore, a re-encoding method, which is integrated with the encryption of IPM, is also proposed to remove drift error. Compared with a previous technique that uses encryption of IPM, the proposed re-encoding method offers savings in the bitrate overhead while completely removing the drift error. Experimental results and analysis based on H.264/AVC were carried out to verify the effectiveness of the proposed methods. In addition, a spiral binary mask mechanism is proposed that can reduce the bitrate overhead incurred by flagging the position of the privacy region. A definition of the syntax structure for the spiral binary mask is given. As a result of the proposed techniques, the privacy regions in a video sequence can be effectively protected by the enhanced scrambling effect with no drift error and a lower bitrate overhead.

Protection of privacy in biometric data

Biometrics is commonly used in many automated veri cation systems offering several advantages over traditional veri cation methods. Since biometric features are associated with individuals, their leakage will violate individuals' privacy, which can cause serious and continued problems as the biometric data from a person are irreplaceable. To protect the biometric data containing privacy information, a number of privacy-preserving biometric schemes (PPBSs) have been developed over the last decade, but they have various drawbacks. The aim of this paper is to provide a comprehensive overview of the existing PPBSs and give guidance for future privacy-preserving biometric research. In particular, we explain the functional mechanisms of popular PPBSs and present the state-of-the-art privacy-preserving biometric methods based on these mechanisms. Furthermore, we discuss the drawbacks of the existing PPBSs and point out the challenges and future research directions in PPBSs.

Protection of big data privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data.

Privacy and security in open and trusted health information systems

The Open and Trusted Health Information Systems (OTHIS) Research Group has formed in response to the health sector’s privacy and security requirements for contemporary Health Information Systems (HIS). Due to recent research developments in trusted computing concepts, it is now both timely and desirable to move electronic HIS towards privacy-aware and security-aware applications. We introduce the OTHIS architecture in this paper. This scheme proposes a feasible and sustainable solution to meeting real-world application security demands using commercial off-the-shelf systems and commodity hardware and software products.

Using SITDRM for Privacy Rights Management

SITDRM 1 is a privacy protection system that protects private data through the enforcement of MPEG REL licenses provided by consumers. Direct issuing of licenses by consumers has several usability problems that will be mentioned in this paper. Further, we will describe how SITDRM incorporates P3P language to provide a consumer-centered privacy protection system.

Strengthening and formally verifying privacy in identity management systems

In a digital world, users’ Personally Identifiable Information (PII) is normally managed with a system called an Identity Management System (IMS). There are many types of IMSs. There are situations when two or more IMSs need to communicate with each other (such as when a service provider needs to obtain some identity information about a user from a trusted identity provider). There could be interoperability issues when communicating parties use different types of IMS. To facilitate interoperability between different IMSs, an Identity Meta System (IMetS) is normally used. An IMetS can, at least theoretically, join various types of IMSs to make them interoperable and give users the illusion that they are interacting with just one IMS. However, due to the complexity of an IMS, attempting to join various types of IMSs is a technically challenging task, let alone assessing how well an IMetS manages to integrate these IMSs. The first contribution of this thesis is the development of a generic IMS model called the Layered Identity Infrastructure Model (LIIM). Using this model, we develop a set of properties that an ideal IMetS should provide. This idealized form is then used as a benchmark to evaluate existing IMetSs. Different types of IMS provide varying levels of privacy protection support. Unfortunately, as observed by Jøsang et al (2007), there is insufficient privacy protection in many of the existing IMSs. In this thesis, we study and extend a type of privacy enhancing technology known as an Anonymous Credential System (ACS). In particular, we extend the ACS which is built on the cryptographic primitives proposed by Camenisch, Lysyanskaya, and Shoup. We call this system the Camenisch, Lysyanskaya, Shoup - Anonymous Credential System (CLS-ACS). The goal of CLS-ACS is to let users be as anonymous as possible. Unfortunately, CLS-ACS has problems, including (1) the concentration of power to a single entity - known as the Anonymity Revocation Manager (ARM) - who, if malicious, can trivially reveal a user’s PII (resulting in an illegal revocation of the user’s anonymity), and (2) poor performance due to the resource-intensive cryptographic operations required. The second and third contributions of this thesis are the proposal of two protocols that reduce the trust dependencies on the ARM during users’ anonymity revocation. Both protocols distribute trust from the ARM to a set of n referees (n > 1), resulting in a significant reduction of the probability of an anonymity revocation being performed illegally. The first protocol, called the User Centric Anonymity Revocation Protocol (UCARP), allows a user’s anonymity to be revoked in a user-centric manner (that is, the user is aware that his/her anonymity is about to be revoked). The second protocol, called the Anonymity Revocation Protocol with Re-encryption (ARPR), allows a user’s anonymity to be revoked by a service provider in an accountable manner (that is, there is a clear mechanism to determine which entity who can eventually learn - and possibly misuse - the identity of the user). The fourth contribution of this thesis is the proposal of a protocol called the Private Information Escrow bound to Multiple Conditions Protocol (PIEMCP). This protocol is designed to address the performance issue of CLS-ACS by applying the CLS-ACS in a federated single sign-on (FSSO) environment. Our analysis shows that PIEMCP can both reduce the amount of expensive modular exponentiation operations required and lower the risk of illegal revocation of users’ anonymity. Finally, the protocols proposed in this thesis are complex and need to be formally evaluated to ensure that their required security properties are satisfied. In this thesis, we use Coloured Petri nets (CPNs) and its corresponding state space analysis techniques. All of the protocols proposed in this thesis have been formally modeled and verified using these formal techniques. Therefore, the fifth contribution of this thesis is a demonstration of the applicability of CPN and its corresponding analysis techniques in modeling and verifying privacy enhancing protocols. To our knowledge, this is the first time that CPN has been comprehensively applied to model and verify privacy enhancing protocols. From our experience, we also propose several CPN modeling approaches, including complex cryptographic primitives (such as zero-knowledge proof protocol) modeling, attack parameterization, and others. The proposed approaches can be applied to other security protocols, not just privacy enhancing protocols.

Privacy by information accountability for e-health systems

Privacy has become one of the main impediments for e-health in its advancement to providing better services to its consumers. Even though many security protocols are being developed to protect information from being compromised, privacy is still a major issue in healthcare where privacy protection is very important. When consumers are confident that their sensitive information is safe from being compromised, their trust in these services will be higher and would lead to better adoption of these systems. In this paper we propose a solution to the problem of patient privacy in e-health through an information accountability framework could enhance consumer trust in e-health services and would lead to the success of e-health services.

Privacy compliance verification in cryptographic protocols

To provide privacy protection, cryptographic primitives are frequently applied to communication protocols in an open environment (e.g. the Internet). We call these protocols privacy enhancing protocols (PEPs) which constitute a class of cryptographic protocols. Proof of the security properties, in terms of the privacy compliance, of PEPs is desirable before they can be deployed. However, the traditional provable security approach, though well-established for proving the security of cryptographic primitives, is not applicable to PEPs. We apply the formal language of Coloured Petri Nets (CPNs) to construct an executable specification of a representative PEP, namely the Private Information Escrow Bound to Multiple Conditions Protocol (PIEMCP). Formal semantics of the CPN specification allow us to reason about various privacy properties of PIEMCP using state space analysis techniques. This investigation provides insights into the modelling and analysis of PEPs in general, and demonstrates the benefit of applying a CPN-based formal approach to the privacy compliance verification of PEPs.

Privacy architectures in social networks’ state-of-the-art survey

The use of social networking has exploded, with millions of people using various web- and mobile-based services around the world. This increase in social networking use has led to user anxiety related to privacy and the unauthorised exposure of personal information. Large-scale sharing in virtual spaces means that researchers, designers and developers now need to re-consider the issues and challenges of maintaining privacy when using social networking services. This paper provides a comprehensive survey of the current state-of-the-art privacy in social networks for both desktop and mobile uses and devices from various architectural vantage points. The survey will assist researchers and analysts in academia and industry to move towards mitigating many of the privacy issues in social networks.