There is often a misalignment between requirements for keeping data owners' information private and real data processing practices, and this can lead to violations of privacy. Specifying and implementing appropriate policies to control a user's access to a system and its resource is critical for keeping data owners' information private. Traditionally, policy specification is isolated from requirements analysis, which often results in data processing practices that are not in compliance with data owners' requirements. This thesis investigates a development scheme that integrates policy specification into requirements analysis and approach design. It suggests that, while we derive specification from requirements analysis, we can also improve requirements and approach design through privacy preservation specification by clarifying ambiguities in the requirements and resolving inconsistencies between requirements and data processing practices. This claim is supported by the requirements analysis and specification of a purpose based access control approach for privacy preservation. The purpose-based access control method consists of an entity of purpose, which expresses requirements for keeping personal information private from a data owner's point of view. The requirements analysis is helped by the specification of the entities, the relationships, the invariants corresponding to the requirements, and the model operations along with proof obligations of their satisfiability. That specification results in a complete purpose based access control model in the case of an intra-organisation scenario. The development scheme has also been applied for privacy preservation in distributed collaborative environments. Distributed computing environments pose further challenges for keeping personal information private. Design considerations are taken for ensuring that personal information is accessed from two or more parties only if agreed privacy policies and privacy preferences are satisfied, and for facilitating privacy policies matching and privacy preference compliance among distributed collaborative organisations. The work presented in this thesis should be of value to researchers on privacy protection methods, to whom the purpose-based access control model has been made available for privacy property verification, and to researchers on privacy specification, who will be able to incorporate specification into the requirements analysis.

In the last few decades, there has been a dramatic uptick in the issues related to protecting user privacy in released data, both in statistical databases and anonymized records. Privacy-preserving data publishing is a field established to handle these releases while avoiding the problems that plagued many earlier attempts. This issue is of particular importance for governmental data, where both the release and the privacy requirements are frequently governed by legislature (e.g., HIPAA, FERPA, Clery Act). This problem is doubly compounded by the complex survey methods employed to counter problems in data collection. The preeminent definition for privacy is that of differential privacy, which protects users by limiting the impact that any individual can have on the result of any query. <br><br>The thesis proposes models for differentially private versions of current survey methodologies and, discusses the evaluation of those models. We focus on the issues of missing data and weighting which are common techniques employed in complex surveys to counter problems with sampling and response rates. First we propose a model for answering queries on datasets with missing data while maintaining differential privacy. Our model uses k-Nearest Neighbor imputation to replicate donor values while protecting the privacy of the donor. Our model provides significantly better bias reduction in realistic experiments using existing data, as well as providing less noise than a naive solution. Our second model proposes a method of performing Iterative Proportional Fitting (IPF) in a differentially private manner, a common technique used to ensure that survey records are weighted consistently with known values. We also focus on the general philosophical need to incorporate privacy when creating new survey methodologies, rather than assuming that privacy can simply be added at a later step.

Proving and verifying remotely a user's identity information have become a critical and challenging problem in the online world, with the increased number of sensitive services offered online. The digital identity management ecosystem has been evolving over the years to address this problem. However, the limitations in existing identity management approaches in handling this problem in a privacy preserving and secure manner have caused disruptions to users' digital lives and damages to revenue and reputation of service providers.<br><br>In this dissertation, we analyze different areas of the identity management ecosystem in terms of privacy and security. In our analysis, we observe three critical aspects to take into account when identifying the privacy and security requirements to address in identity management scenarios, namely: i) protecting privacy and security of digital identity and online transactions of users; ii) providing other stakeholders with assurance about user identity information and accountability of transactions; iii) preserving utility (e.g. accuracy, efficiency and deployability).<br>We show that existing authentication models and identity management protocols fail to address critical privacy and security requirements related to all these three aspects, mainly because of inherent conflicts among these requirements. <br>For example, existing authentication protocols, which aim to protect service providers from imposters by involving strong authentication factors, such as biometrics, fail to protect privacy and security of users' biometrics. Protecting an identity management system against counterfeits of identity assets, while preserving unlinkability of the transactions carried out using the identity assets, is another example of conflicting yet critical privacy and security requirements.<br>We demonstrate that careful combinations of cryptographic techniques and other technologies make it feasible to design privacy preserving identity management protocols which address critical and conflicting requirements related to the aforementioned three aspects. Certain techniques, that we have developed for these protocols, are independent contributions with applications beyond the domain of digital identity management. We validate our contributions by providing prototype implementations, experimental evaluations and security proofs.

In this dissertation, we studied and addressed the privacy-preserving and authentication techniques for some network applications, where existing internet security solutions cannot address them straightforwardly due to different trust and attack models and possibly constrained resources. For example, in a centralized dynamic spectrum access (DSA) system, the spectrum resource licensees called incumbent users (IUs), have strong operational privacy requirements for the DSA service provider called spectrum access system (SAS), and hence SAS is required to perform spectrum computation without knowing IUs' operational information. This means SAS can at most be considered as a semi-trusted party which is honest but curious, and common anonymization and end-to-end encryption cannot address this issue, and dedicated solutions are required. Another example is that in an intra-vehicle Controller Area Network (CAN), the transmitter can only embed 64 bits of message and its authentication tag into on message frame, which makes it difficult to achieve message authentication in real-time with sufficient cryptographic strength. The focus of this dissertation is to fill the gap of existing solutions with stronger security notion and practicability. On the topic of privacy-preserving DSA systems, we firstly explored existing solutions and proposed a comparative study. We additionally proposed a new metric for evaluation and showed the advantages and disadvantages of existing solutions. We secondly studied the IU location privacy in 3.5GHz band ESC-based DSA system and proposed a novel scheme called PriDSA. PriDSA addresses malicious colluding SAS attack model through leveraging different and relatively lightweight cryptography primitive with novel design, granting stronger security notion and improved efficiency as well. We thirdly studied the operational privacy of both IU and secondary users (SUs) in a general centralized SAS based DSA system and proposed a novel framework called PeDSS. Through our novel design that integrates differential privacy with secure multi-party computation protocol, PeDSS exhibits great communication and computation overhead compared to existing solutions. On the topic of lightweight message authentication in resource-constrained networks, we firstly explored message authentication schemes with high cryptographic strength and low communication-overhead and proposed a novel scheme called CuMAC. CuMAC provides a flexible trade-off between authentication delay and cryptographic strength, through the embodiment of a novel concept that we refer to as accumulation of cryptographic strength. We secondly explored the possibility of achieving both high cryptographic strength and low authentication delay and proposed a variant of CuMAC called CuMAC/S. By employing the novel idea of message speculation, CuMAC/S achieves enables the accumulation of cryptographic strength while incurring minimal delay when the message speculation accuracy is high. / Doctor of Philosophy / The privacy-preserving and message authentication issues of some network applications are distinctive from common internet security due to different attack models and possibly constrained resources, and these security and privacy concerns cannot be addressed by applying existing internet security solutions straightforwardly. For example, in a centralized dynamic spectrum access (DSA) system, the spectrum resource licensees called incumbent users (IUs), have strong operational privacy requirements for the DSA service provider called spectrum access system (SAS), and hence SAS is required to perform spectrum computation without knowing IUs' operational information. This means SAS can at most be considered as a semi-trusted party which is honest but curious, and common anonymization and end-to-end encryption cannot address this issue, and dedicated solutions are required. Another example is that in an intra-vehicle Controller Area Network (CAN), the transmitter can only embed 64 bits of message and its authentication tag into on message frame, which makes it difficult to achieve message authentication in real-time with sufficient cryptographic strength. We addressed the privacy issue of DSA systems by proposing novel schemes incorporating efficient cryptographic primitives and various privacy-preserving techniques, achieving a greatly higher efficiency or stronger privacy-preserving level. We addressed the lightweight authentication issue of resource-constrained networks by employing the novel concept of security accumulation and message speculation, achieving high cryptographic strength, low communication overhead, and probable low latency.

This dissertation addresses the challenge of enabling accurate analysis of network data while ensuring the protection of network participants' privacy. This is an important problem: massive amounts of data are being collected (facebook activity, email correspondence, cell phone records), there is huge interest in analyzing the data, but the data is not being shared due to concerns about privacy. Despite much research in privacy-preserving data analysis, existing technologies fail to provide a solution because they were designed for tables, not networks, and cannot be easily adapted to handle the complexities of network data. We develop several technologies that advance us toward our goal. First, we develop a framework for assessing the risk of publishing a network that has been "anonymized." Using this framework, we show that only a small amount of background knowledge about local network structure is needed to re-identify an "anonymous" individual. This motivates our second contribution: an algorithm that transforms the structure of the network to provably lower re-identification risk. In comparison with other algorithms, we show that our approach more accurately preserves important features of the network topology. Finally, we consider an alternative paradigm, in which the analyst can analyze private data through a carefully controlled query interface. We show that the degree sequence of a network can be accurately estimated under strong guarantees of privacy.

No description available.

With the advent of smart devices, Internet of things and cloud computing the amount of information collected about an individual is enormous. Using this meta-data, a complete profile about a person could be created - professional information, personal information like his/her choices, preferences, likes/dislikes etc. The concept of privacy is totally lost with this gamut of technology. The ability to separate one's on-line identity from their personal identity is near impossible. The conflicting interests of the two parties - service providers' need for authentication and the users' privacy needs - is the cause for this problem. Privacy Preserving Authentication could help solve both these problems by creating valid and anonymous identities for the users. And simply by proving the authenticity and integrity of this anonymous identity (without revealing/exposing it) the users can obtain services whilst protecting their privacy. In this thesis, I review and analyze the various types of PPA schemes leading to the discussion of our new scheme 'Lightweight Anonymous Attestation with Efficient Revocation'. Finally, the scenarios where these schemes are applicable are discussed in detail. / Master of Science

According to the Federal Communications Commission's rules and recommendations set forth for the 3.5 GHz Citizens Broadband Radio Service, a three-tiered structure shall govern the newly established shared wireless band. The three tiers are comprised of three different levels of spectrum access; Incumbent Access, Priority Access and General Authorized Access. In accordance and fulfillment with this dynamic spectrum access framework, we present the inclusion of Priority Access tier into a two-tiered privacy-preserving ESC-based dynamic spectrum access system. / Master of Science / With the development of wireless communication technologies, the number of wireless communication reliant applications has been increasing. Most of these applications require dedicated spectrum frequencies as communication channels. As such, the radio frequency spectrum, utilized and allocated for these wireless applications, is depleting. This problem can be alleviated by adopting dynamic spectrum access schemes. The current static spectrum allocation scheme assigns designated spectrum frequencies to specific users. This static frequency management approach leads to inefficient frequency utilization as the occupation of frequency channels may vary depending upon time periods. Dynamic spectrum access schemes allow unlicensed users opportunistic access to vacant spectrum spaces. Thus, the adoption of these spectrum sharing schemes will increase the efficiency of spectrum utilization, and slow down the spectrum depletion. However, the design and implementation of these schemes face different challenges. These spectrum sharing systems need to guarantee the privacy of the involved parties while maintaining specific functionalities required and recommended by the Federal Communications Commission. In this thesis, we present the inclusion of a three-tiered frame, approved by the Federal Communications Commission, into a privacy-preserving dynamic spectrum system.

Technological advances have led to the generation of large databases of organizational data recognized as an information-rich, strategic asset for internal analysis and sharing with trading partners. Data mining techniques can discover patterns in large databases including relationships considered strategically relevant to the owner of the data. The frequent item set hiding problem is an area of active research to study approaches for hiding the sensitive knowledge patterns before disclosing the data outside the organization. Several methods address hiding sensitive item sets including an exact approach that generates an extension to the original database that, when combined with the original database, limits the discovery of sensitive association rules without impacting other non-sensitive information. To generate the database extension, this method formulates a constraint optimization problem (COP). Solving the COP formulation is the dominant factor in the computational resource requirements of the exact approach. This dissertation developed heuristics that address the scalability of the exact hiding method. The heuristics are directed at improving the performance of COP solver by reducing the size of the COP formulation without significantly affecting the quality of the solutions generated. The first heuristic decomposes the COP formulation into multiple smaller problem instances that are processed separately by the COP solver to generate partial extensions of the database. The smaller database extensions are then combined to form a database extension that is close to the database extension generated with the original, larger COP formulation. The second heuristic evaluates the revised border used to formulate the COP and reduces the number of variables and constraints by selectively substituting multiple item sets with composite variables. Solving the COP with fewer variables and constraints reduces the computational cost of the processing. Results of heuristic processing were compared with an existing exact approach based on the size of the database extension, the ability to hide sensitive data, and the impact on nonsensitive data.

Privacy preserving distributed data mining aims to design secure protocols which allow multiple parties to conduct collaborative data mining while protecting the data privacy. My research focuses on the design and implementation of privacy preserving two-party protocols based on homomorphic encryption. I present new results in this area, including new secure protocols for basic operations and two fundamental privacy preserving data mining protocols. I propose a number of secure protocols for basic operations in the additive secret-sharing scheme based on homomorphic encryption. I derive a basic relationship between a secret number and its shares, with which we develop efficient secure comparison and secure division with public divisor protocols. I also design a secure inverse square root protocol based on Newton's iterative method and hence propose a solution for the secure square root problem. In addition, we propose a secure exponential protocol based on Taylor series expansions. All these protocols are implemented using secure multiplication and can be used to develop privacy preserving distributed data mining protocols. In particular, I develop efficient privacy preserving protocols for two fundamental data mining tasks: multiple linear regression and EM clustering. Both protocols work for arbitrarily partitioned datasets. The two-party privacy preserving linear regression protocol is provably secure in the semi-honest model, and the EM clustering protocol discloses only the number of iterations. I provide a proof-of-concept implementation of these protocols in C++, based on the Paillier cryptosystem.