Melihat Peran Perantara dalam Kasus Penyebaran Video Non-Konsensual dengan Kerangka Contextual Integrity

Jumlah kasus kekerasan berbasis gender online atau KBGO selama masa pandemi dilaporkan meningkat. Penambahan jumlah pengguna internet serta kian masifnya tranformasi digital selama masa pandemi dipandang berkontribusi dalam peningkatan kasus KBGO tersebut. Kekerasan berbasis gender yang selama ini sudah marak terjadi di ranah offline menemukan ruang baru di dunia online sehingga kian mengancam keamanan, kesehatan dan keselamatan perempuan. Dalam kasus KBGO, dua pihak yang paling banyak dibicarakan adalah pelaku dan korban. Namun, pembicaraan tersebut melupakan pihak lain yang juga berpengaruh penting dalam terjadinya kekerasan, yakni platform digital yang menjadi wadah maupun memfasilitasi peristiwa kekerasan. Platform digital seperti media sosial tercatat menjadi salah satu medium terjadinya KBGO. Tulisan ini memaparkan peran platform digital sebagai perantara dalam kasus kekerasan, khususnya KBGO, dengan menerapkan kerangka contextual integrity yang diajukan Nissenbaum (2010) pada kasus penyebaran video intim non-konsensual yang menimpa GA dan GL. Tulisan ini menunjukkan bahwa melalui sistem dan kebijakan layanannya, pihak perantara turut berperan dalam mendorong terjadinya KBGO. === The number of online gender based violence (OGBV) cases in Indonesia are reportedly increasing during the pandemic. The increasing number of internet users and the massive digital transformation during the pandemic has contributed to the escalation in OGBV cases. Gender-based violence has found a new space in the online world, thus threatening women’s security, health, and safety. In the OGBV case, the two parties that has been discussed the most were the perpetrators and the victims. However, the discussion forgot about the other party that also had an important influence, namely the digital platform. Digital platfarm like social media has become the medium for OGBV.This paper describes the role of the digital platform as an intermediary party for communication exchange in the cases of online violence, especially OGBV, by applying the contextual integrity framework proposed by Nissenbaum (2010) in the dissemination of non-consensual intimate videos of GA and GL cases. This paper shows that through its system and policies, digital platforms play significant role in facilitating OGBV.

FROM WATCHED AT WORK TO WATCHED AT HOME: WORKPLACE SURVEILLANCE DURING A PANDEMIC

The COVID-19 pandemic has created new opportunities and new tensions related to workplace surveillance. Monitoring workers via digital tools to analyze everything from keystrokes to email and social media to the websites they visit is increasingly common, and the shift to remote work in the early days of the pandemic led many employers to consider new ways to monitor their employees while working from home. In this paper, we consider how the pandemic has affected office workers’ experience of surveillance, focusing on the types of monitoring they currently experience and their concerns related to future forms of surveillance. In particular, we unpack the sociotechnical implications of shifting work surveillance practices due to COVID-19, focusing on how evolving and emergent workplace surveillance practices may impact workers. Using factorial vignettes, survey respondents (N=645) read and responded to 35 scenarios about future workplace surveillance practices. Each scenario randomly varied four factors about workplace monitoring: the type of data being collected, the purpose for data collection, the actors who can access the data, and the transmission principle guiding data collection. For each scenario, respondents assessed both the appropriateness of each scenario and how concerning they found it. We evaluate this data, as well as data about respondents’ work environment before and during the pandemic, using Nissenbaum’s framework of privacy as contextual integrity. We also consider the potential harms associated with different types of monitoring.

Strengthening Children’s Privacy Literacy through Contextual Integrity

Researchers and policymakers advocate teaching children about digital privacy, but privacy literacy has not been theorized for children. Drawing on interviews with 30 families, including 40 children, we analyze children’s perspectives on password management in three contexts—family life, friendship, and education—and develop a new approach to privacy literacy grounded in Nissenbaum’s contextual integrity framework. Contextual integrity equates privacy with appropriate flows of information, and we show how children’s perceptions of the appropriateness of disclosing a password varied across contexts. We explain why privacy literacy should focus on norms rather than rules and discuss how adults can use learning moments to strengthen children’s privacy literacy. We argue that equipping children to make privacy-related decisions serves them better than instructing them to follow privacy-related rules.

Making context the central concept in privacy engineering

There is a gap between people’s online sharing of personal data and their concerns about privacy. Till now, this gap is addressed by attempting to match individual privacy preferences with service providers’ options for data handling. This approach has ignored the role different contexts play in data sharing. This paper aims at giving privacy engineering a new direction putting context centre stage and exploiting the affordances of machine learning in handling contexts and negotiating data sharing policies. This research is explorative and conceptual, representing the first development cycle of a design science research project in privacy engineering. The paper offers a concise understanding of data privacy as a foundation for design extending the seminal contextual integrity theory of Helen Nissenbaum. This theory started out as a normative theory describing the moral appropriateness of data transfers. In our work, the contextual integrity model is extended to a socio-technical theory that could have practical impact in the era of artificial intelligence. New conceptual constructs such as ‘context trigger’, ‘data sharing policy’ and ‘data sharing smart contract’ are defined, and their application is discussed from an organisational and technical level. The constructs and design are validated through expert interviews; contributions to design science research are discussed, and the paper concludes with presenting a framework for further privacy engineering development cycles.

Identification of Fictitious Messages in Social Network using E-Hits and Newsapi

Social network has become a primary resource for users to send and receive the foremost up-to-date data and trend the present events. Currently, most of the social network contains the fictional content that was created by the influential spreaders wherever the message originality and therefore the spreader identity cannot be found which affects the end users. The proposed models to discover fictitious messages are verifying the contextual integrity with the trained classifier using large datasets. But the problem lies in updating of datasets with the recent or trending events from trusted sources in a regular interval. In the existing model, Hypertext-Induced Topic Search (HITS) method has been used for rating posts based on hub score and authority score. The hub score is calculated based on how many posts are posted or liked or tagged by the user and authority score is calculated based on how many users liked or tagged a post. If the user who ranks high in hub score tries to trend the low ranked post in authority score, the user will be marked as spreader. But the problem lies in the identification and verification of the posts that ranks in authority score. In our proposed system, we have enhanced the HITS algorithm by adding a third mechanism called top score which assigns weightage for every post based on the time they have posted. The time and content of the post has been verified by theintegrated new model NewsAPI. Based on the three scores, the posts are filtered and matched with the news collected from NewsAPI. The news or posts that have not been matched either with the context or with the time will be marked as fictitious.