Collaborative filtering is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today’s systems use centralized databases and have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to share all kinds of information about places and things to do, see and buy, but the privacy risks are severe. In this paper we introduce a peer-to-peer protocol for collaborative filtering which protects the privacy of individual data. A second contribution of this paper is a new collaborative filtering algorithm based on factor analysis which appears to be the most accurate method for CF to date. The new algorithm has other advantages in speed and storage over previous algorithms. It is based on a careful probabilistic model of user choice, and on a probabilistically sound approach to dealing with missing data. Our experiments on several test datasets show that the algorithm is more accurate than previously reported methods, and the improvements increase with the sparseness of the dataset. Finally, factor analysis with privacy is applicable to other kinds of statistical analyses of survey or questionaire data scientists (e.g. web surveys or questionaires).

Server-based collaborative filtering systems have been very successful in e-commerce and in direct recommendation applications. In future, they have many potential applications in ubiquitous computing settings. But today's schemes have problems such as loss of privacy, favoring retail monopolies, and with hampering diffusion of innovations. We propose an alternative model in which users control all of their log data. We describe an algorithm whereby a community of users can compute a public "aggregate" of their data that does not expose individual users' data. The aggregate allows personalized recommendations to be computed by members of the community, or by outsiders. The numerical algorithm is fast, robust and accurate. Our method reduces the collaborative filtering task to an iterative calculation of the aggregate requiring only addition of vectors of user data. Then we use homomorphic encryption to allow sums of encrypted vectors to be computed and decrypted without exposing individual data. We give verification schemes for all parties in the computation. Our system can be implemented with untrusted servers, or with additional infrastructure, as a fully peer-to-peer (P2P) system. 1

Abstract. Understanding how to implement SPI successfully is arguably the most challenging issue facing the SPI field today. The SPI literature contains many case studies of successful companies and descriptions of their SPI programs. However, there has been no systematic attempt to synthesize and organize the prescriptions offered. The research efforts to date are limited and inconclusive and without adequate theoretical and psychometric justification. This paper provides a synthesis of prescriptions for successful quality management and process improvement found from an extensive review of the quality management, organizational learning, and software process improvement literature. The literature review was confirmed by empirical studies among both researchers and practitioners. The main result is an instrument for measuring the key factors of success in SPI based on data collected from 120 software organizations. The measures were found to have satisfactory psychometric properties. Hence, managers can use the instrument to guide SPI activities in their respective organizations and researchers can use it to build models to relate the facilitating factors to both learning processes and SPI outcomes.

This paper summarizes the history of software engineering technology transfer and suggests ways to help both practitioners and researchers understand how to shorten the time between innovation and effective practice. Here, we use the term "technology" to encompass a large number of things, and it is important for us to understand what "technologies" to study. For example, software engineers use a variety of techniques or methods to build and maintain software. We use the terms method or technique to mean a formal procedure for producing some result. By contrast, a "tool" is an instrument, language or automated system for accomplishing something in a better way. This better way can mean that the tool makes us more accurate, more efficient, or more productive, or that it enhances the quality of the resulting product. However, a tool is not always necessary for making something well. For example, a cooking technique can make a sauce better, not the pot or spoon used by the chef. (Pfleeger 1998) A procedure is like a recipe: a combination of tools and techniques that, in concert, produce a particular product. For instance, test plans describe test procedures; they tell us which tools will be used on which data sets under which circumstances so that we can determine whether our software meets its requirements. Like a cooking style, a paradigm

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An important goal of most empirical software engineering research is the transfer of research results to industrial applications. Two important obstacles for this transfer are the lack of control of variables of case studies, i.e., the lack of explanatory power, and the lack of realism of controlled experiments. While it may be difficult to increase the explanatory power of case studies, there is a large potential for increasing the realism of controlled software engineering experiments. To convince industry about the validity and applicability of the experimental results, the tasks, subjects and the environments of the experiments should be as realistic as practically possible. Such experiments are, however, more expensive than experiments involving students, small tasks and pen-andpaper environments. Consequently, a change towards more realistic experiments requires a change in the amount of resources spent on software engineering experiments. This paper argues that software engineering researchers should apply for resources enabling expensive and realistic software engineering experiments similar to how other researchers apply for resources for expensive software and hardware that are necessary for their research. The paper describes experiences from recent experiments that varied in size from involving one software professional for 5 days to 130 software professionals, from 9 consultancy companies, for one day each.

In this paper we argue that there is a necessity for automating modifications to legacy assets. We propose a five layered process for the introduction and employment of tool support that enables automated modification to entire legacy systems. Furthermore, we elaborately discuss each layer on a conceptual level, and we make appropriate references to sources where technical contributions supporting that particular layer can be found. We sketch the perspective that more and more people working in the software engineering area will be contributing to working on existing systems and/or tools to support such work. Categories and Subject Description: D.2.6 [Software Engineering]: Programming Environments—Interactive;

Because knowledge plays an important role in the creation of wealth, economic actors often wish to skew the flow of knowledge in their favor. Managers seek to spread knowledge widely within their organization but prevent its diffusion to rivals. Regional planners promote knowledge diffusion within a local economy but not beyond it. We ask, when will knowledge developed in one area of dense social connections – such as a firm, a geographic locale, or a technological community – tend to diffuse to the edge of that area but not beyond it? Marrying social network theory with a view of knowledge transfer as a search process, we argue that the degree of knowledge inequality across social boundaries depends crucially on the nature

paper was made possible through support provided by the Office of Population,

In 1953, a young female Macaque monkey in the south of Japan washed a muddy sweet potato in a stream before eating it. This obvious improvement in food preparation was imitated quickly by other monkeys and in less than 10 years it became the norm in her immediate group; by 1983, the method had diffused completely. In 1956, the same monkey