Stefano Bennati

ETH Zurich | ETH Zürich · Computational Social Science

Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data coll...

Malware reverse-engineering, specifically, identifying the tasks a given piece of malware was designed to perform (e.g., logging keystrokes, recording video, establishing remote access) is a largely human-driven process that is a difficult and time-consuming operation. In this chapter, we present an automated method to identify malware tasks using...

The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a d...

Collective sensing is an emergent phenomenon that enables individuals to estimate an invisible property of the environment through the observation of social interactions. It has been shown in Nature that individuals in animal groups develop the capacity to perceive this signal and the social interactions between them produce complex group behavior...

Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investiga...

Several smart city services rely on users contribution, e.g., data, which can be costly for the users in terms of privacy. High costs lead to reduced user participation, which undermine the success of smart city technologies. This work develops a scenario-independent design principle, based on public good theory, for resource management in smart ci...

The effect of phenotypic plasticity on evolution, the so-called Baldwin effect, has been studied extensively for more than 100 years. Plasticity is known to influence the speed of evolution towards a specific genetic configuration, but whether it also influences what that genetic configuration is, is still an open question. This question is investi...

This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning method for reducing the privacy cost of information transmitted through a network. Distributed sensor networks are often used for automated classification and detection of abnormal events in high-stakes situations, e.g. fire in buildings, earthquakes, or crowd disasters. Su...

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate info...

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate info...

Collective sensing is an emergent phenomenon which enables individuals to estimate a hidden property of the environment through the observation of social interactions. Previous work on collective sensing shows that gregarious individuals obtain an evolutionary advantage by exploiting collective sensing when competing against solitary individuals. T...

Identifying the tasks a given piece of malware was designed to perform (e.g. logging keystrokes, recording video, establishing remote access, etc.) is a difficult and time-consuming operation that is largely human-driven in practice. In this paper, we present an automated method to identify malware tasks. Using two different malware collections, we...

An important source of constraints on unified theories of cognition is their ability to perform complex tasks that are challenging for humans. Malware reverse-engineering is an important type of analysis in the domain of cyber-security. Rapidly identifying the tasks that a piece of malware is designed to perform is an important part of reverse engi...

Malware reverse-engineering is an important type of analysis in cybersecurity. Rapidly identifying the tasks that a piece of malware is designed to perform is an important part of reverse engineering that is generally manually performed as it relies heavily on human intuition This paper describes how the use of cognitively-inspired inference can as...

ACT-R is one of the most widely used cognitive architectures, and it has been used to model hundreds of phenomena described in the cognitive psychology literature. In spite of this, there are relatively few studies that have attempted to apply ACT-R to situations involving social interaction. This is an important omission since the social aspects o...

This paper describes a general instance-based learning model of sensemaking in the context of geospatial intelligence tasks. Building upon a model previously described in Lebiere, Pirolli, Thomson et al. (2013), our model captures human performance across two tasks involving generating and updating likelihoods based on simulated geospatial intellig...

In this paper, we describe a framework for processing big data that maximizing the efficiency of human data scientists by having them primarily operate over information that is best structured to human processing demands. We accomplish this through the use of cognitive models as an intermediary between machine learning algorithms and human data sci...

Robotic swarms are nonlinear dynamical systems that benefit from the presence of human operators in realistic missions with changing goals and constraints. There has been recent interest in safe operations of robotic swarms under human control. Verification and validation techniques for these human-machine systems could be deployed to provide forma...