Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Economic location-based services, privacy and the relationship to identity

Mobile telephony and mobile internet are driving a new application paradigm: location-based services (LBS). Based on a person’s location and context, personalized applications can be deployed. Thus, internet-based systems will continuously collect and process the location in relationship to a personal context of an identified customer. One of the challenges in designing LBS infrastructures is the concurrent design for economic infrastructures and the preservation of privacy of the subjects whose location is tracked. This presentation will explain typical LBS scenarios, the resulting new privacy challenges and user requirements and raises economic questions about privacy-design. The topics will be connected to “mobile identity” to derive what particular identity management issues can be found in LBS

Privacy Design in Online Social Networks: Learning from Privacy Breaches and Community Feedback

The objective of this paper is to systematically develop privacy heuristics for Online Social Network Services (SNS). In order to achieve this, we provide an analytical framework in which we characterize privacy breaches that have occurred in SNS and distinguish different stakeholders’ perspectives. Although SNS have been criticized for numerous grave privacy breaches, they have also proven to be an interesting space in which privacy design is implemented and critically taken up by users. Community involvement in the discovery of privacy breaches as well as in articulating privacy demands points to possibilities in user-driven privacy design. In our analysis we take a multilateral security analysis approach and identify conflicts in privacy interests and list points of intervention and negotiation. In our future research, we plan to validate the usefulness as well as the usability of these heuristics and to develop a framework for privacy design in SNS

Information Makes A Difference For Privacy Design

In the current information age, information can make a difference to all aspects of one’s life, emotionally, eth ically, financially or societally . Information privacy plays a key role in enabling a difference in many dimensions such as trust, respect, reputation, security, resource, ability, employment, etc. The capability of information to make a difference to one’s life is a fundamental factor; and privacy status of information is a key factor driving this difference. Understanding the impact of these two factors to one’s life within an IS context is an import ant research gap in the discipline. This paper studies “information + privacy”, ontologically and integrally, in making a difference to one’s life, within the IS context. In recognition of the importance of the Privacy- by -Design approach to IS development, a methodology is proposed to understand the grounds of information and model fundamental constructs for using Privacy- by - Design approach to develop robust privacy - friendly information systems

Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology

Crowdsourcing Privacy Design Critique: An Empirical Evaluation of Framing Effects

When designed incorrectly, information systems can thwart people’s expectations of privacy. An emerging technique for evaluating systems during the development stage is the crowdsourcing design critique, in which design evaluations are sourced using crowdsourcing platforms. However, we know that information framing has a serious effect on decision-making and can steer design critiques in one way or another. We investigate how the framing of design cases can influence the outcomes of privacy design critiques. Specifically, we test whether -˜Personas’, a central User-Centered Design tool for describing users, can inspire empathy in users while criticizing privacy designs. In an experiment on Amazon Mechanical Turk workers (n=456), we show that describing design cases by using personas causes intrusive designs to be criticized more harshly. We discuss how our results can be used to enhance privacy-by-design processes and encourage user-centered privacy engineering

Contextual Integrity of A Virtual (Reality) Classroom

The multicontextual nature of immersive VR makes it difficult to ensure contextual integrity of VR-generated information flows using existing privacy design and policy mechanisms. In this position paper, we call on the HCI community to do away with lengthy disclosures and permissions models and move towards embracing privacy mechanisms rooted in Contextual Integrity theory.Comment: 11 pages, CHI'23 Workshop - Designing Technology and Policy Simultaneousl

Gradient-tracking Based Differentially Private Distributed Optimization with Enhanced Optimization Accuracy

Privacy protection has become an increasingly pressing requirement in distributed optimization. However, equipping distributed optimization with differential privacy, the state-of-the-art privacy protection mechanism, will unavoidably compromise optimization accuracy. In this paper, we propose an algorithm to achieve rigorous $\epsilon$-differential privacy in gradient-tracking based distributed optimization with enhanced optimization accuracy. More specifically, to suppress the influence of differential-privacy noise, we propose a new robust gradient-tracking based distributed optimization algorithm that allows both stepsize and the variance of injected noise to vary with time. Then, we establish a new analyzing approach that can characterize the convergence of the gradient-tracking based algorithm under both constant and time-varying stespsizes. To our knowledge, this is the first analyzing framework that can treat gradient-tracking based distributed optimization under both constant and time-varying stepsizes in a unified manner. More importantly, the new analyzing approach gives a much less conservative analytical bound on the stepsize compared with existing proof techniques for gradient-tracking based distributed optimization. We also theoretically characterize the influence of differential-privacy design on the accuracy of distributed optimization, which reveals that inter-agent interaction has a significant impact on the final optimization accuracy. The discovery prompts us to optimize inter-agent coupling weights to minimize the optimization error induced by the differential-privacy design. Numerical simulation results confirm the theoretical predictions