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

POTs: Protective Optimization Technologies

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems. We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial. We propose Protective Optimization Technologies (POTs). POTs provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic-beating applications, and recalibrating credit scoring for loan applicants.Comment: Appears in Conference on Fairness, Accountability, and Transparency (FAT* 2020). Bogdan Kulynych and Rebekah Overdorf contributed equally to this work. Version v1/v2 by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was presented at HotPETS 2018 and at PiMLAI 201

Spiny CACTOS: OSN Users Attitudes and Perceptions Towards Cryptographic Access Control Tools

<p>Cryptographic access control tools for online social networks (CACTOS) allow users to enforce their privacy settings online without relying on the social network provider or any other third party. Many such tools have been proposed in the literature, some of them implemented and currently publicly available, and yet they have seen poor or no adoption at all. In this paper we investigate which obstacles may be hindering the adoption of these tools. To this end, we perform a user study to inquire users about key issues related to the desirability and general perception of CACTOS. Our results suggest that, even if social network users would be potentially interested in these tools, several issues would effectively obstruct their adoption. Participants in our study perceived that CACTOS are a disproportionate means to protect their privacy online. This in turn may have been motivated by the explicit use of cryptography or the fact that users do not actually share on social networks the type of information they would feel the need to encrypt. Moreover, in this paper we point out to several key elements that are to be considered for the improvement and better usability of CACTOS.</p

Decentralized Privacy-Preserving Proximity Tracing.

timestamp: Tue, 21 Jul 2020 00:40:32 +0200 biburl: https://dblp.org/rec/journals/debu/TroncosoPHSLLSP20.bib bibsource: dblp computer science bibliography, https://dblp.orgstatus: publishe