Abstract
Social media are transforming relationships with customers for all sectors, including tourism. Since the search for information is a critical aspect of tourist purchase decision process, the importance of social media for tourism is evident. However, the presence of tourism brands in social media is not enough to have an impact on tourist purchase decisions: it is necessary to generate engagement. This chapter aims to conceptualize tourist engagement on social media and identify tourist engagement indicators. Tourist engagement was conceptualized through a literature review that identified four dimensions of engagement: popularity, commitment, virality, and post engagement. A set of indicators is proposed to measure tourist engagement in each of these dimensions. The proposed TSM engagement framework was validated through a mixed-method approach, using secondary data and interviews carried out with Brazilian tourist destinations.

Abstract
This chapter aims to contribute to a better understanding of the role of websites in business internationalization by exploring how website overall objectives and their coherence with website strategies support website internationalization effectiveness. It provides empirical evidence on the experiences of Portuguese companies shared by 20 managers of large companies and SMEs of various activity sectors. Results show the importance of a clear website strategy (e.g., clear objectives and coherent tactics) for an effective role in internationalization. Findings also confirm that, while many managers are skeptic about the effectiveness of websites as an internationalization touchpoint, namely due to sector characteristics (e.g., type of customers, type of products/services), the website is perceived as an essential tool for reaching, attracting, and involving international customers, supporting other communication instruments such as participating in international fairs and sales force.

Abstract
Hashtags are commonly used in social media communication not only to categorize conversations but particularly to raise attention and generate debate of certain topics. Hashtag activism is one of the areas that is gaining particular attention from academics and the overall society. The focus of this chapter is hashtag attributes. Particularly, it analyses and compares four hashtags related to violence against women that circulated on social networks during the COVID-19 pandemic: #16Days, #IsolatedNotAlone, #womensupportingwomen, and #NiUnaMenos. The chapter highlights important aspects to increase the effectiveness of communication with the use of hashtags.

Abstract
As blockchains go beyond cryptocurrencies into applications in multiple industries such as Insurance, Healthcare and Banking, handling personal or sensitive data, data access control becomes increasingly relevant. Access control mechanisms proposed so far are mostly based on requester identity, particularly for permissioned blockchain platforms, and are limited to binary, all-or-nothing access decisions. This is the case with Hyperledger Fabric's native access control mechanisms and, as permission updates require consensus, these fall short regarding the flexibility required to address GDPR-derived policies and client consent management. We propose SDAM, a novel access control mechanism for Fabric that enables fine-grained and dynamic control policies, using both contextual and resource attributes for decisions. Instead of binary results, decisions may also include mandatory data transformations as to conform with the expressed policy, all without modifications to Fabric. Results show that SDAM's overhead w.r.t baseline Fabric is acceptable. The scalability of the approach w.r.t to the number of concurrent clients is also evaluated and found to follow Fabric's.

Abstract
Tor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy.

Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.