Anticipating the actions of others is key to coordinating joint activities. We propose the notion of anticipatory action and perception for for robots acting with humans. We describe four systems in which anticipation has been modeled for human-robot interaction; two in a teamwork setting, and two in a human-robot joint performance setting. In evaluating the effects of anticipatory agent activity, we find in one study that anticipation aids in team efficiency, as well as in the perceived commitment of the robot to the team and its contribution to the team's fluency and success. In another study we see anticipatory action and perception affect the human partner's sense of team fluency, the team's improvement over time, the robot’s contribution to the efficiency and fluency, the robot's intelligence, and the robot’s adaptation to the task. We also find that subjects working with the anticipatory robot attribute more human qualities to the robot, such as gender and intelligence.

Representing human behavior and cognition, from individuals to societies, presents a range of challenges to the modeling and simulation community. A common thread through many of these challenges is formulating an authentic representation of time. Many of the issues related to time representation, from the sequencing of cognitive decision processes and information processing, to communication and interaction between agents, to the longer term time scales associated with ideas such as belief revision, remain open research areas throughout the community. The inherent variability between human subjects makes generalization difficult even with data from designed experiments. Discrete event simulation (DES) provides a well-documented alternative to time-step simulation and shows potential for applications across the domain of human behavior representation. This paper provides an overview of a modular discrete event framework for social simulation, along with the social and behavioral theories underlying the currently implemented modules. We discuss the practical challenges presented by time in the representation of human cognition, and provide a case study analysis of the output of the discrete event social simulation.

There is a growing awareness and interest in the issues of accountability and transparency in the pursuit of digital privacy. In previous work, we asserted that systems needed to be “policy aware” and able to compute the likely compliance of any digital transaction with the associated privacy policies (law, rule, or contract). This paper focuses on one critical step in respecting privacy in a digital environment, that of understanding the context associated with each digital transaction. For any individual transaction, the pivotal fact may be context information about the data, the party seeking to use it, the specific action to be taken, or the associated rules. We believe that the granularity of semantic web representation is well suited to this challenge and we support this position in the paper.

One of the most significant challenges in achieving digital privacy is incorporating privacy policy directly in computer systems. While rule systems have long existed, translating privacy laws, regulations, policies, and contracts into processor amenable forms is slow and difficult because the legal text is scattered, run-on, and unstructured, antithetical to the lean and logical forms of computer science. We are using and developing intermediate isomorphic forms as a Rosetta Stone-like tool to accelerate the translation process and in hopes of providing support to future domain-specific Natural Language Processing technology. This report describes our experience, thoughts about how to improve the form, and discoveries about the form and logic of the legal text that will affect the successful development of a rules tool to implement real-world complex privacy policies.

In this essay I argue that it is logically and practically possible to secure the right to privacy under conditions of increasing social transparency. The argument is predicated on a particular analysis of the right to privacy as the right to the personal space required for the exercise of practical rationality. It also rests on the distinction between the unidirectional transparency required by repressive governments and the increasing omnidirectional transparency that liberal information societies are experiencing today. I claim that a properly administered omnidirectional transparency will not only enhance privacy and autonomy, but can also be a key development in the creation of a society that is more tolerant of harmless diversity and temperate in its punishment of anti-social behaviors.

Territorial privacy is an old concept for privacy of the personal space dating back to the 19th century. Despite its former relevance, territorial privacy has been neglected in recent years, while privacy research and legislation mainly focused on the issue of information privacy. However, with the prospect of smart and ubiquitous environments, territorial privacy deserves new attention. Walls, as boundaries between personal and public spaces, will be insufficient to guard territorial privacy when our environments are permeated with numerous computing and sensing devices, that gather and share real-time information about us. Territorial privacy boundaries spanning both the physical and virtual world are required for the demarcation of personal spaces in smart environments. In this paper, we analyze and discuss the issue of territorial privacy in smart environments. We further propose a real-time user-centric observation model to describe multimodal observation channels of multiple physical and virtual observers. The model facilitates the definition of a territorial privacy boundary by separating desired from undesired observers, regardless of whether they are physically present in the user’s private territory or virtually participating in it. Moreover, we outline future research challenges and identify areas of work that require attention in the context of territorial privacy in smart environments.

Information is shared more extensively when a user can confidently classify all his information according to its desired degree of disclosure prior to transmission. While high quality classification is relatively straightforward for structured data (e.g., credit card numbers, cookies, "confidential" reports), most consumer and business information is unstructured (e.g., Facebook posts, corporate email). All current technological approaches to classifying unstructured information seek to identify only that information having the desired characteristics (i.e., to maximize the percentage of filtered content that requires privacy protection). Such focus on boosting classifier Precision (P) causes technology solutions to miss sensitive information [i.e., Recall (R) is compromised for the sake of P improvement]. Such privacy protection will fall short of user expectations no matter how "intelligent" the technology may be in extending beyond keywords to user meaning. Systems must simultaneously optimize both P and R in order to protect privacy sufficiently to encourage the free flow of personal and corporate information. This requires a socio-technical methodology wherein the user is intimately involved in iterative privacy improvement. The approach is a general one in which the classifier can be modified as necessary at any time when sampling measures of P and R deem it appropriate. Matching the ever-evolving user privacy model to the technology solution (e.g., active machine learning) affords a technique for building and maintaining user trust.

The privacy dangers of data proliferation on the Web are well-known. Information on the Web has facilitated the deanonymization of anonymous bloggers, the de-sanitization of government records and the identification of individuals based on search engine queries. What has received less attention is Web-mining in support of privacy. In this position paper we argue that the very ability ofWeb data to breach privacy demonstrates its value as a laboratory for the detection of privacy breaches before they happen. In addition, we argue that privacy-invasive services may become privacy-respecting by mining publicly available Web data, with little decrease in performance and efficiency.

Some of the top legal ontologies developed so far include the Functional Ontology for Law [FOLaw] The increasing need for legal information and content (Valente 1995), the Frame-Based Ontology (van Kralingen management caused by the growing amount of 1995), the LRI-Core ontology (Breuker 2004), unstructured (or poorly structured) legal data managed by DOLCE CLO [Core Legal Ontology] (Gangemi et al. legal publishing companies, law firms and public 2003), or the Ontology of Fundamental Concepts (Rubino administrations, or the increasing amount of legal et al. 2006, Sartor 2006) the basis for the LKIF-Core information directly available on the World Wide Web, Ontology (Breuker et al. 2007). Nevertheless, most legal have created an urgent need to construct conceptual ontologies are domain specific ontologies, which represent structures for knowledge representation to share and particular legal domains towards search, indexing and manage intelligently all this information, whilst making reasoning in a specific domain of national or European law human-machine communication and understanding (e.g. the IPRONTO ontology by Delgado et al. 2003, the possible.

Online social lending is the Web 2.0's response to classical bank loans. Borrowers publish credit applications on websites which match them with private investors. We point to a conflict between economic interests and privacy goals in online social lending, empirically analyze the effect of data disclosure on credit conditions, and outline directions towards efficient yet privacy-friendly alternative credit markets.