Related papers: Do We Need Subsidiarity in Software?
In the current technology environment, users are often in a vulnerable position when it comes to protecting their privacy. Previous efforts to promote privacy protection have largely focused on top-down approaches such as regulation and…
Nowadays we are faced with an increasing popularity of social software including wikis, blogs, micro-blogs and online social networks such as Facebook and MySpace. Unfortunately, the mostly used social services are centralized and personal…
In modern datasets, where single records can have multiple owners, enforcing user-level differential privacy requires capping each user's total contribution. This "contribution bounding" becomes a significant combinatorial challenge.…
Improved software discovery is a prerequisite for greater software reuse: after all, if someone cannot find software for a particular task, they cannot reuse it. Understanding people's approaches and preferences when they look for software…
Cloud computing is an Internet-based computing, where shared resources, software and information, are provided to computers and devices on-demand. It provides people the way to share distributed resources and services that belong to…
As computer systems become more and more complex, software and tools lag more and more behind. This is especially true for scientific software that often demands high performance, and thus needs to take advantage of parallelisms, memory…
Context: Software-intensive organizations' rationale for sharing Open Source Software (OSS) may be driven by both idealistic, strategic and commercial objectives, and include both monetary as well as non-monetary benefits. To gain the…
Privacy has traditionally been a major motivation for distributed problem solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as Distributed Constraint Optimization Problem (DCOP) are fundamental models used to solve…
Data sharing enables critical advances in many research areas and business applications, but it may lead to inadvertent disclosure of sensitive summary statistics (e.g., means or quantiles). Existing literature only focuses on protecting a…
A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Privacy can be rigorously quantified using the framework of {\em differential privacy}, which…
A growing framework of legal and ethical requirements limit scientific and commercial evalua-tion of personal data. Typically, pseudonymization, encryption, or methods of distributed com-puting try to protect individual privacy. However,…
Logs are one of the most fundamental resources to any security professional. It is widely recognized by the government and industry that it is both beneficial and desirable to share logs for the purpose of security research. However, the…
Clustering software into peer groups based on its apparent functionality allows for simple, intuitive categorization of software that can, in particular, help identify which software uses comparatively more privilege than is necessary to…
This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a…
The spread of information networks has not only made it easier for people to access a variety of information sources but also greatly enhanced the ability of individuals to disseminate information. Unfortunately, however, the problem of…
Recommender systems are essential for personalizing digital experiences on e-commerce sites, streaming services, and social media platforms. While these systems are necessary for modern digital interactions, they face fairness, bias,…
A typical user interacts with many digital services nowadays, providing these services with their data. As of now, the management of privacy preferences is service-centric: Users must manage their privacy preferences according to the rules…
Social networks have become an essential meeting point for millions of individuals willing to publish and consume huge quantities of heterogeneous information. Some studies have shown that the data published in these platforms may contain…
Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals' privacy is adequately protected. There is no consolidated standard…
Privacy is an increasingly important aspect of data publishing. Reasoning about privacy, however, is fraught with pitfalls. One of the most significant is the auxiliary information (also called external knowledge, background knowledge, or…