The Case for Animal Privacy in the Design of Technologically Supported Environments

Privacy is an essential consideration when designing interactive systems for humans. However, at a time when interactive technologies are increasingly targeted at non-human animals and deployed within multispecies contexts, the question arises as to whether we should extend privacy considerations to other animals. To address this question, we revisited early scholarly work on privacy, which examines privacy dynamics in non-human animals (henceforth “animals”). Then, we analysed animal behaviour literature describing privacy-related behaviours in different species. We found that animals use a variety of separation and information management mechanisms, whose function is to secure their own and their assets' safety, as well as negotiate social interactions. In light of our findings, we question tacit assumptions and ordinary practises that involve human technology and that affect animal privacy. Finally, we draw implications for the design of interactive systems informed by animals' privacy requirements and, more broadly, for the development of privacy-aware multispecies interaction design.

Private identity agreement for private set functionalities

Private set intersection and related functionalities are among the most prominent real-world applications of secure multiparty computation. While such protocols have attracted significant attention from the research community, other functionalities are often required to support a PSI application in practice. For example, in order for two parties to run a PSI over the unique users contained in their databases, they might first invoke a support functionality to agree on the primary keys to represent their users. This paper studies a secure approach to agreeing on primary keys. We introduce and realize a functionality that computes a common set of identifiers based on incomplete information held by two parties, which we refer to as private identity agreement, and we prove the security of our protocol in the honest-but-curious model. We explain the subtleties in designing such a functionality that arise from privacy requirements when intending to compose securely with PSI protocols. We also argue that the cost of invoking this functionality can be amortized over a large number of PSI sessions, and that for applications that require many repeated PSI executions, this represents an improvement over a PSI protocol that directly uses incomplete or fuzzy matches.

Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.

A Review on Privacy Requirements and Application Layer Security in Internet of Things (IoT)

Internet of Things (IoT) is the predominant emerging technology that targets on facilitating interconnection of internet-enabled resources. IoT applications concentrate on automating different tasks that facilitate physical objects to act autonomously without any human interventions. The emerging and current IoT applications are determined to be highly significant for improving the degree of efficiency, comfort and automation for its users. Any kind of security breach on the system will directly influences the life of the humans In this paper, a comprehensive review on Privacy requirements and application layer Security in Internet of Things (IoT) is presented for exploring the possible security issues in IoT that could be launched over the individual layers of IoT architecture. This review explores different challenges of classical security solutions that are related to authentication, key management and cryptographic solutions.It also presents the details of existing access control and device authentication schemes with their pros and cons.