An architecture for certification-aware service discovery

Service-orientation is an emerging paradigm for building complex systems based on loosely coupled components, deployed and consumed over the network. Despite the original intent of the paradigm, its current instantiations are limited to a single trust domain (e.g., a single organization). Also, some of the key promises of service-orientation - such as the dynamic orchestration of externally provided software services, using runtime service discovery and deployment - are still unachieved. One of the main reasons for this is the trust gap that normally arises when software services, offered by previously unknown providers, are to be selected at run-time, without any human intervention. To close this gap, the concept of machine-readable security certificates (called asserts) has been recently introduced, which paves the way to automated processing about security properties of services. Similarly to current security certification schemes, the assessment of the security properties of a service is delegated to an independent third party (certification authority), who issues a corresponding assert, bound to the service. In this paper, we propose an architecture, which exploits the assert concept to realise a certification-aware service discovery framework. The architecture supports the discovery of single services based on certified security properties (in additional to the usual functional properties), as well as the dynamic synthesis of service compositions, that satisfy the given security properties. The architecture is extensible, thus allowing for a range of domain specific matchmaking components, to cover dimensions related to, e.g., performance, cost and other non-functional characteristics

Privacy-Preserving Access Control in Electronic Health Record Linkage

Sharing aggregated electronic health records (EHRs) for integrated health care and public health studies is increasingly demanded. Patient privacy demands that anonymisation procedures are in place for data sharing. However traditional methods such as k-anonymity and its derivations are often over-generalizing resulting in lower data accuracy. To tackle this issue, we present the Semantic Linkage K-Anonymity (SLKA) approach supporting ongoing record linkages. We show how SLKA balances privacy and utility preservation through detecting risky combinations hidden in data releases

Towards Self-Awareness Privacy Protection for Internet of Things Data Collection

The Internet of Things (IoT) is now an emerging global Internet-based information architecture used to facilitate the exchange of goods and services. IoT-related applications are aiming to bring technology to people anytime and anywhere, with any device. However, the use of IoT raises a privacy concern because data will be collected automatically from the network devices and objects which are embedded with IoT technologies. In the current applications, data collector is a dominant player who enforces the secure protocol that cannot be verified by the data owners. In view of this, some of the respondents might refuse to contribute their personal data or submit inaccurate data. In this paper, we study a self-awareness data collection protocol to raise the confidence of the respondents when submitting their personal data to the data collector. Our self-awareness protocol requires each respondent to help others in preserving his privacy. The communication (respondents and data collector) and collaboration (among respondents) in our solution will be performed automatically

Security and Privacy Preservation in Mobile Advertising

Mobile advertising is emerging as a promising advertising strategy, which leverages prescriptive analytics, location-based distribution, and feedback-driven marketing to engage consumers with timely and targeted advertisements. In the current mobile advertising system, a third-party ad broker collects and manages advertisements for merchants who would like to promote their business to mobile users. Based on its large-scale database of user profiles, the ad broker can help the merchants to better reach out to customers with related interests and charges the merchants for ad dissemination services. Recently, mobile advertising technology has dominated the digital advertising industry and has become the main source of income for IT giants. However, there are many security and privacy challenges that may hinder the continuous success of the mobile advertising industry. First, there is a lack of advertising transparency in the current mobile advertising system. For example, mobile users are concerned about the reliability and trustworthiness of the ad dissemination process and advertising review system. Without proper countermeasures, mobile users can install ad-blocking software to filter out irrelevant or even misleading advertisements, which may lower the advertising investments from merchants. Second, as more strict privacy regulations (e.g. European General Data Privacy Regulations) take effect, it is critical to protect mobile users’ personal profiles from illegal sharing and exposure in the mobile advertising system. In this thesis, three security and privacy challenges for the mobile advertising system are identified and addressed with the designs, implementations, and evaluations of a blockchain-based architecture. First, we study the anonymous review system for the mobile advertising industry. When receiving advertisements from a specific merchant (e.g. a nearby restaurant), mobile users are more likely to browse the previous reviews about the merchant for quality-of-service assessments. However, current review systems are known for the lack of system transparency and are subject to many attacks, such as double reviews and deletions of negative reviews. We exploit the tamper-proof nature and the distributed consensus mechanism of the blockchain technology, to design a blockchain-based review system for mobile advertising, where review accumulations are transparent and verifiable to the public. To preserve user review privacy, we further design an anonymous review token generation scheme, where users are encouraged to leave reviews anonymously while still ensuring the review authenticity. We also explore the implementation challenges of the blockchain-based system on an Ethereum testing network and the experimental results demonstrate the application feasibility of the proposed anonymous review system. Second, we investigate the transparency issues for the targeted ad dissemination process. Specifically, we focus on a specific mobile advertising application: vehicular local advertising, where vehicular users send spatial-keyword queries to ad brokers to receive location-aware advertisements. To build a transparent advertising system, the ad brokers are required to provide mobile users with explanations on the ad dissemination process, e.g., why a specific ad is disseminated to a mobile user. However, such transparency explanations are often found incomplete and sometimes even misleading, which may lower the user trust on the advertising system if without proper countermeasures. Therefore, we design an advertising smart contract to efficiently realize a publicly verifiable spatial-keyword query scheme. Instead of directly implementing the spatial-keyword query scheme on the smart contract with prohibitive storage and computation cost, we exploit the on/off-chain computation models to trade the expensive on-chain cost for cheap off-chain cost. With two design strategies: digest-and-verify and divide-then-assemble, the on-chain cost for a single spatial keyword query is reduced to constant regardless of the scale of the spatial-keyword database. Extensive experiments are conducted to provide both on-chain and off-chain benchmarks with a verifiable computation framework. Third, we explore another critical requirement of the mobile advertising system: public accountability enforcement against advertising misconducts, if (1) mobile users receive irrelevant ads, or (2) advertising policies of merchants are not correctly computed in the ad dissemination process. This requires the design of a composite Succinct Non-interactive ARGument (SNARG) system, that can be tailored for different advertising transparency requirements and is efficient for the blockchain implementations. Moreover, pursuing public accountability should also achieve a strict privacy guarantee for the user profile. We also propose an accountability contract which can receive explanation requirements from both mobile users and merchants. To promote prompt on-chain responses, we design an incentive mechanism based on the pre-deposits of involved parties, i.e., ad brokers, mobile users, and merchants. If any advertising misconduct is identified, public accountability can be enforced by confiscating the pre-deposits of the misbehaving party. Comprehensive experiments and analyses are conducted to demonstrate the versatile functionalities and feasibility of the accountability contract. In summary, we have designed, implemented, and evaluated a blockchain-based architecture for security and privacy preservations in the mobile advertising. The designed architecture can not only enhance the transparency and accountability for the mobile advertising system, but has also achieved notably on-chain efficiency and privacy for real-world implementations. The results from the thesis may shed light on the future research and practice of a blockchain-based architecture for the privacy regulation compliance in the mobile advertising

Privacy in the Smart City - Applications, Technologies, Challenges and Solutions

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities

mPSAuth: Privacy-Preserving and Scalable Authentication for Mobile Web Applications

As nowadays most web application requests originate from mobile devices, authentication of mobile users is essential in terms of security considerations. To this end, recent approaches rely on machine learning techniques to analyze various aspects of user behavior as a basis for authentication decisions. These approaches face two challenges: first, examining behavioral data raises significant privacy concerns, and second, approaches must scale to support a large number of users. Existing approaches do not address these challenges sufficiently. We propose mPSAuth, an approach for continuously tracking various data sources reflecting user behavior (e.g., touchscreen interactions, sensor data) and estimating the likelihood of the current user being legitimate based on machine learning techniques. With mPSAuth, both the authentication protocol and the machine learning models operate on homomorphically encrypted data to ensure the users' privacy. Furthermore, the number of machine learning models used by mPSAuth is independent of the number of users, thus providing adequate scalability. In an extensive evaluation based on real-world data from a mobile application, we illustrate that mPSAuth can provide high accuracy with low encryption and communication overhead, while the effort for the inference is increased to a tolerable extent.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl