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Privacy is an important issue that has raised particular concerns among many research areas. This issue dramatically increases with the proliferation of the Web services composition paradigm. This is mainly due to the high dynamism and untrustworthiness characteristics of the services to be composed, which impose high levels of risk on the interacting parties. Existing technologies for managing and...
Confidentiality of training data induced by releasing machine-learning models, and has recently received increasing attention. Motivated by existing MI attacks and other previous attacks that turn out to be MI "in disguise," this paper initiates a formal study of MI attacks by presenting a game-based methodology. Our methodology uncovers a number of subtle issues, and devising a rigorous...
Radio frequency identification (RFID) is a useful technology that has myriad applications in retail, manufacturing, and healthcare settings. RFID can scan devices in their proximity and report the data to information systems. RFID in healthcare settings presents potential security and privacy concerns to the people and processes being tracked by the devices -- particularly healthcare workers including...
The control and protection of user data is a very important aspect in the design and deployment of the Internet of Things (IoT). The heterogeneity of the IoT technologies, the number of the participating devices and systems, and the different types of users and roles create important challenges in the IoT context. In particular, requirements of scalability, interoperability and privacy are difficult...
We investigate the relation between t-closeness, a well-known model of data anonymization, and alpha-protection, a model of data discrimination. We show that t-closeness implies bd(t)-protection, for a bound function bd() depending on the discrimination measure at hand. This allows us to adapt an inference control method, the Mondrian multidimensional generalization technique, to the purpose of non-discrimination...
Data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns...
In last years, data mining has raised some concerns related to privacy invasion of the individuals and potential discrimination based on the extracted patterns and profiles. Efforts at fighting against these risks have led to developing privacy preserving data mining (PPDM) techniques and anti-discrimination techniques in data mining. However, there is an evident gap between the large body of research...
Information technology has attracted considerable attention in modern automobiles for their promise of value-added services. Based on increasing connectivity and seamless integration of advanced functionality into vehicles, a new challenge is the development of holistic and standardized privacy approaches. So far, privacy has often been considered as a singular task, neglecting the impact of a holistic...
Many location-based software applications have been developed for mobile devices. Consequently, location-based service providers often have the detailed trajectory history of their service recipients. The collected spatial-temporal information of their service recipients can be invaluable for other organizations and companies in many aspects, for example, direct marking, market analysis, and consumer...
Internet has gained huge popularity over the last decade. It offers its users reliable, efficient and exciting online services. However, the users reveal a lot of their personal information by using these services. Websites that collect information state their practices with data in their privacy policies. However, it is difficult to ensure if the policies are enforced properly in their practices...
Data as a Service (DaaS) builds on service-oriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new concerns that traditional privacy models for Web services do not handle. First, the distinction between the roles of service providers and data providers is unclear, leaving the latter helpless for specifying and verifying the enforcement of...
Collection and analysis of personal information is among the most far-reaching developments in online retail practices. While the potential value of harnessing data about people is expected to improve the online service offerings, it raises reasonable concerns about privacy. Rather than cutting off opportunities to make personal data available for enhancing online services, we introduce a model where...
We describe a new paradigm for articulating need-to-protect and need-to-share policies that shows promise for enabling automated derivation of the downgrading rulesets needed to comply with these policies in systems that share data. This new paradigm is based on fine-grained semantic policy specifications in terms of context, content, Purpose, and Anti-purpose that are expressed in a machine-understandable...
Situation-Based Access Control (SitBAC) is a conceptual model for representing access control policies of healthcare organizations by characterizing situations of access to patient data. The SitBAC model enables formal representation of access situations as an ontology of concepts (Patient, Data Requestor, EHR, Task, and Response) along with their attributes and relationships. A competing access control...
In sensor networks, privacy can be addressed in different levels of the network stack and at different points of the information flow. This paper presents an application level scheme for controlling information disclosure at the points of data capture. The scheme includes a trust model for facilitating in-network privacy decisions. The trust model exploits the pre-deployment knowledge on the network...
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