Linda Pareschi

Is ontology-based activity recognition really effective?

While most activity recognition systems rely on data-driven approaches, the use of knowledge-driven techniques is gaining increasing interest. Research in this field has mainly concentrated on the use of ontologies to specify the semantics of activities, and ontological reasoning to recognize them based on context information. However, at the time of writing, the experimental evaluation of these techniques is limited to computational aspects; their actual effectiveness is still unknown. As a first step to fill this gap, in this paper, we experimentally evaluate the effectiveness of the ontological approach, using an activity dataset collected in a smart-home setting. Preliminary results suggest that existing ontological techniques underperform data-driven ones, mainly because they lack support for reasoning with temporal information. Indeed, we show that, when ontological techniques are extended with even simple forms of temporal reasoning, their effectiveness is comparable to the one of a state-of-the-art technique based on Hidden Markov Models. Then, we indicate possible research directions to further improve the effectiveness of ontology-based activity recognition through temporal reasoning.

Privacy in Georeferenced Context-Aware Services: A Survey

Location based services (LBS) are a specific instance of a broader class of Internet services that are predicted to become popular in a near future: context-aware services. The privacy concerns that LBS have raised are likely to become even more serious when several context data, other than location and time, are sent to service providers as part of an Internet request. This paper provides a classification and a brief survey of the privacy preservation techniques that have been proposed for this type of services. After identifying the benefits and shortcomings of each class of techniques, the paper proposes a combined approach to achieve a more comprehensive solution for privacy preservation in georeferenced context-aware services.

Preserving Anonymity of Recurrent Location-Based Queries

The anonymization of location based queries through the generalization of spatio-temporal information has been proposed as a privacy preserving technique. We show that the presence of multiple concurrent requests, the repetition of similar requests by the same issuers, and the distribution of different service parameters in the requests can significantly affect the level of privacy obtained by current anonymity-based techniques. We provide a formal model of the privacy threat, and we propose an incremental defense technique based on a combination of anonymity and obfuscation. We show the effectiveness of this technique by means of an extensive experimental evaluation.

Efficient profile aggregation and policy evaluation in a middleware for adaptive mobile applications

There is a large consensus on the need for a middleware to efficiently support adaptation in pervasive and mobile computing. Advanced forms of adaptation require the aggregation of context data and the evaluation of policy rules that are typically provided by multiple sources. This paper addresses the problem of designing the reasoning core of a middleware that supports these tasks, while guaranteeing very low response times as required by mobile applications. Technically, the paper presents strategies to deal with conflicting rules, algorithms that implement the strategies, and algorithms that detect and solve potential rule cycles. A detailed experimental analysis supports the theoretical results and shows the applicability of the resulting middleware in large-scale applications.

Protecting Users' Anonymity in Pervasive Computing Environments

The large scale adoption of adaptive services in pervasive and mobile computing is likely to be conditioned to the availability of reliable privacy-preserving technologies. Unfortunately, the research in this field can still be considered in its infancy. This paper considers a specific pervasive computing scenario, and shows that the application of state-of-the-art techniques for the anonymization of service requests is insufficient to protect the privacy of users. A specific class of attacks, called shadow attacks, is formally defined and a set of defense techniques is proposed. These techniques are validated through the use of a simulator and an extensive set of experiments.

Anonymity and Diversity in LBS: A Preliminary Investigation

This paper presents a preliminary investigation on the notions of anonymity and diversity in the context of location based services, with the goal of identifying relevant parameters to be included in privacy preserving user preferences

Shadow attacks on users' anonymity in pervasive computing environments

Privacy preserving technologies are likely to become an essential component of adaptive services in pervasive and mobile computing. Although privacy issues have been studied for a long time in computer science as well as in other fields, most studies are focused on the release of data from large repositories. Mobile and pervasive computing pose new challenges, requiring specific formal models for attacks and new privacy preserving techniques. This paper considers a specific pervasive computing scenario, and shows that the application of state-of-the-art techniques for the anonymization of service requests is insufficient to protect the privacy of users. A specific class of attacks, called shadow attacks, is formally defined and a defense technique is proposed. This defense is formally proved to be correct, and its effectiveness is validated by extensive experiments in a simulated environment.

Towards the adaptive integration of multiple context reasoners in pervasive computing environments

The pervasive computing vision consists in realizing ubiquitous technologies to support the execution of peoples everyday tasks by proactively providing appropriate information and services in a natural and transparent way based on the current context. Hence, a fundamental ingredient of pervasive computing is a mechanism to recognize the current high level context of users based on lower level context data provided, for instance, by body-worn and environmental sensors. Given the variability of encountered contextual conditions, the currently available data sources are highly dynamic; hence, context reasoning should continuously adapt to the change of available sources. In this paper we propose a technique to dynamically discover sources of context data, and to modularly integrate reasoners that use those data to infer higher level context information. Our proposal is corroborated by an implementation on mobile devices and sensors, and by an experimental evaluation showing its efficiency and effectiveness.

Integrating Identity, Location, and Absence Privacy in Context-Aware Retrieval of Points of Interest

The retrieval of close-by points of interest (POIs) is becoming a popular location-based service (LBS), often integrated with navigational services and geo-social networks. However, the access to POI services is prone to potentially serious privacy issues, since requests for POIs often include sensitive information like the users location and her personal interests. Many techniques to enforce privacy in LBS have been proposed in the literature, in some cases focusing on anonymizing the requests and in others on obfuscating information in order to decrease its sensitivity. In many cases privacy protection comes at some cost in terms of service precision and performance. In this paper we propose a novel technique that combines the above cited approaches, overcomes some of their limitations in terms of assumptions on adversary knowledge, while still guaranteeing service precision. Our privacy solution has been integrated in an existing distributed system to share and retrieve POIs based not only on the users current location but also on other (possibly sensitive) context data.