Previous Proceedings

The proceedings are published in the ACM Digital Library as part of the ACM SAC

List of the accepted contributions of previous ACM SAC TRECK tracks since 2005:

  • ACM SAC TRECK 2005
    • Strong Pseudonymous Communication for Peer-to-Peer Reputation Systems, Michael, Kinateder; Ralf, Terdic; Kurt, Rothermel
    • Simulation of a Distributed Recommendation System for Pervasive Networks, Seamus, Moloney
    • Component Based Trust Management in the Context of a Virtual Organization, Yanjun, Zuo; Brajendra, Panda
    • A Trust-enhanced Recommender System application: Moleskiing, Paolo, Avesani; Paolo, Massa; Roberto, Tiella
    • How to Incorporate Revocation Status Information into the Trust Metrics for Public Key Certification, Kemal, Bicakci; Bruno, Crispo; Andrew S., Tanenbaum
  • ACM SAC TRECK 2006
    • Improving Collaborative Filtering with Trust-based Metrics Weng, Jianshu. Miao, Chunyan. Goh, Angela. Nanyang Technological University
    • Trust[ed| in] Computing, Signed Code and the Heat Death of the Internet Poritz, Jonathan. IBM Zurich
    • Trust-Enhanced Visibility for Personalized Document Recommendations Hess, Claudia. Stein, Klaus. Schlieder, Christoph. Bamberg University
    • STRUDEL: Supporting TRUst for Dynamic Emergence of peering coaLitions Quercia, Daniele. Capra, Licia. Lad, Manish. Hailes, Stephen. Saleem, Bhatti. University College London
    • Impact of Peer Incentive on Polluted Content Dissemination Fabricio Benevenuto, Cristiano Costa, Marisa Vasconcelos, Virgilio 
    • Almeida, Jussara Almeida / Miranda Mowbray. Federal University of Minas Gerais / HP Laboratories Bristol
    • Information Trustworthiness Evaluation based on Trust Combination Zuo, Yanjun. Panda, Brajendra. University of North Dakota
    • A Fuzzy Model for Reasoning about Reputation in Web Services Sherchan, Wanita. Wai Loke, Seng. Krishnaswamy, Shonali. Monash University
    • BambooTrust: Practical Scalable Trust Management for Global Public Computing Kotsovinos, Evangelos. / Williams, Aled. Deutsche Telekom Laboratories / University of Cambridge
    • Trust-Decisions on the Base of Maximal Information of Recommended Direct-Trust, Roth, Uwe, Fusenig, Volker, University of Luxembourg
  • ACM SAC TRECK 2007
    • Modeling deceptive information dissemination using a holistic approach Yi Hu, Zhichun Xiao, Brajendra Panda 
    • Evaluating peer-to-peer recommender systems that exploit spontaneous affinities Giancarlo Ruffo, Rossano Schifanella 
    • A protocol to preserve a code of conduct Cristiano Longo, Paolo Giarrusso 
    • Fighting pollution dissemination in peer-to-peer networks Cristiano Costa, Vanessa Soares, Jussara Almeida, Virgilio Almeida 
    • Certain trust: a trust model for users and agents Sebastian Ries 
    • Designing a trust chain for a thin client on a live Linux CD Megumi Nakamura, Seiji Munetoh
  • ACM SAC TRECK 2008
    • Propagating Multitrust within Trust Networks
      • We suggest the concept of multitrust, which is aimed at computing trust by collectively involving a group of trustees at the same time: the trustor needs the concurrent support of multiple individuals to accomplish its task. We propose Soft Constraint Logic Programming based on semirings as a mean to quickly represent and evaluate trust propagation for this scenario. To attain this, we model the Web of Trust adapting it to a weighted and-or graph, where the weight on a connector corresponds to the trust feedback value among the connected peers. Semirings are the parametric and flexible structures used to appropriately represent trust metrics. (Corresponding author: Santini, Francesco)
    • Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms
      • Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results. (Corresponding author: Tso-Sutter, Karen H.L.)
    • The Effect of Correlation Coefficients on Communities of Recommenders
      • Recommendation systems, based on collaborative filtering, offer a means of sifting through the enourmous amounts of content on the web, by composing user ratings in order to generate predicted ratings for other users. This kind of system can be viewed as a network of interacting peers, where each user is a node and the links to all other nodes are weighted according to how similar the corresponding users are. Predicted ratings are generated by a user for unknown items by requesting and aggregating rating information from the surrounding neighbors. However, the different methods of computing user similarity, or weighting the network links, very often do not agree with each other as to the quality of recommendations that a pair of users could exchange, and in doing so they change the entire structure of the network of recommenders. In this work we perform an analysis of a range of similarity measures, comparing their performance in terms of prediction accuracy and coverage, based on the network perspective of a collaborative filtering environment. Using the results we obtain, we argue that user-similarity may not sufficiently capture the relationships that recommenders could otherwise share in order to maximise the performance of these collaborative communities. (Corresponding author: Lathia, Neal)
    • Autonomic trust reasoning enables misbehavior detection in OLSR
      • Ad Hoc networks do not rely on any centralized administration or fixed network infrastructure and their nodes establish a routing structure in a self-organized way, by means of an ad hoc routing protocol such as OLSR. Ad hoc route discovery and maintenance introduce specific security problems for routing protocols to prevent, detect or respond. Solutions to secure these routing protocols using some centralized units or trusted third-parties actually constrain the self-organization of ad hoc networks. In this paper, we propose for OLSR the integration of trust reasonings into each node behavior, so as to allow a self-organized trust-based control to help nodes to detect misbehavior attacks. Our analysis of OLSR brings out the trust rules that characterize this protocol and allows us to express formally the trust-related properties that can be verified by each node to assess the correct behavior of the other nodes. Simulation of OLSR with nodes reasoning on trust allows us to demonstrate the effectiveness of our approach and to compare trust-based routing choices with the bare OLSR reachability-based choices. (Corresponding author: Adnane, Asmaa)
    • Whom Should I Trust? The Impact of Key Figures on Cold Start Recommendations
      • Generating adequate recommendations for newcomers is a hard problem for a recommender system (RS) due to lack of detailed user profiles and social preference data. Empirical evidence suggests that the incorporation of a trust network among the users of the RS can leverage such ‘cold start’ (CS) recommendations. Hence, new users should be encouraged to connect to the network as soon as possible. But whom should new users connect to? Given the impact this choice has on the delivered recommendations, it is critical to guide newcomers through this early stage connection process. In this paper, we identify key figures in the trust network (in particular mavens, connectors and frequent raters) and investigate their influence on the coverage and accuracy of a collaborative filtering RS. Using a dataset from Epinions.com, we demonstrate that the generated recommendations for a new user are more beneficial if they connect to an identified key figure compared to a random user. (Corresponding author: Victor, Patricia)
    • Surework: A Super-peer Reputation Framework for P2P Networks
      • Reputation systems are proved mechanisms used to help nodes to decide whom to trust, to maintain the overall credibility of the system and to promote collaboration. This paper presents Surework, a reputation framework based on Super-peers. In Surework, peers form clusters around Super-reputation-peers (Sure-peers) who help to increase the reputation knowledge. Surework introduces incentives in order to promote that nodes with higher capabilities become Super-peers and assume more tasks than normal peers. Reciprocity is also promoted by encouraging peers to provide better services to most reputable client peers. (Corresponding author: Rodriguez-Perez, Manuel)
    • CAT: A Context-Aware Trust Model for Open and Dynamic Systems
      • The requirements for spontaneous interactions in open and dynamic systems create security issues and necessitate the incorporation of trust management into each software entity to make decisions. Trust encompasses various quality attributes (e.g., security, competence, honesty) and helps in making appropriate decisions. In this paper, we present CAT, an interaction-based Context-Aware Trust model for open and dynamic systems by considering services as contexts. We identify a number of trust properties including context and risk awareness and address those in the proposed model. A context-similarity parameter is proposed to make decisions in similar situations. A time-based ageing parameter is introduced to change trust values over time without any further interaction. We present direct and indirect recommendations and apply path-based ageing on indirect recommendations. A mechanism to calculate the accuracy of recommendations is described. This accuracy is used to differentiate between reliable and unreliable recommendations in the total trust calculation. (Corresponding author: Uddin, Mohammad)
    • Comparing Keywords and Taxonomies in the Representation of Users Profiles in a Content-Based Recommender
      • This work investigates the use of keywords and classes to represent user’s profiles in order to improve a content-based recommender system. The techniques were implemented and tested in a recommender system for a website that gathers commercial ads. Ads are posted by individuals and contain a title and a textual description. Profiles are created and maintained through the analysis of ads seen by the user during a certain period of time and may be represented by classes, keywords or both kinds. Keywords are automatically extracted from the textual description of the ads. Classes come from a taxonomy defined by the website. Ads must be posted within a leaf class of the taxonomy. The items to be recommended are ads containing keywords associated to the user in his/her profile and/or ads classified in the leaf-classes present in the user’s profile. The paper demonstrates that the combination of both techniques (keywords and classes) outperforms the use of each one separately. (Corresponding author: Loh, Stanley)
    • Examining the Motivations of Defection in Large-Scale Open Systems
      • In large-scale open systems such as eBay one of the key concerns in increasing the utility of users is having a trustworthy method for users to determine which interactions will be satisfactory and which are liable to lead to disappointment. Rather than starting from the point of assuming there are "good" and "bad" users we will examine why we can make such a distinction in this context and how humans mitigate some of the problems which seem endemic to such a system through game modification. We then demonstrate a use of this model of behaviour in simulating a particular agent choice in order to show the conditions under which different reputation systems affect an agent's trustworthiness, before briefly describing possible future directions of research to deal with the truly disenfranchised agents. (Corresponding author: Martin-Hughes, Rowan)
  • ACM SAC TRECK 2009
    • iTrustU: A Blog Recommender System based on Multi-Faceted Trust and Collaborative Filtering, Ting-Chun Peng, Institute for Information Industry, Taiwan, Seng-cho T. Chou, National Taiwan University, Taiwan
    • Extending Bayesian Trust Models Regarding Context-Dependence and User Friendly Representation, Sebastian Ries, Technische Universität Darmstadt, Germany
    • A Personalized Framework for Trust Assessment, Trung Dong Huynh, University of Southampton, United Kingdom
    • Defending Online Reputation Systems against Collaborative Unfair Raters through Signal Modeling and Trust, Yafei Yang, University of Rhode Island, United States, Yan (Lindsay) Sun, University of Rhode Island, United States, Steven Kay, University of Rhode Island, United States, Qing Yang, University of Rhode Island, United States
    • Modeling and Analyzing Review Information on the Web Focusing on Credibility, Takuya Kobayashi, Kyoto University, Japan, Hiroaki Ohshima, Kyoto University, Japan, Satoshi Oyama, Kyoto University, Japan, Katsumi Tanaka, Kyoto University, Japan
    • A Taxonomy and Adversarial Model for Attacks against Network Log Anonymizatio, Justin King, IBM Rochester, United States, Kiran Lakkaraju, University of Illinios, United States, Adam Slagell, University of Illinois, United States
  • ACM SAC TRECK 2010
    • Fraud Detection in Reputation Systems in e-Markets using Logistic Regression, Rafael Maranzato, Adriano Pereira, Marden Neubert and Alair Pereira do Lago, Brazil
    • Evaluating Trust in Grid Certificates, David O’Callaghan, Louise Doran and Brian Coghlan, Ireland
    • Computing Multi-Party Trust Privately, Shlomi Dolev, Niv Gilboa and Marina Kopeetsky, Israel
    • iTrust: An Integrated Trust Framework for Wireless Sensor Networks, Kuldeep Yadav, India, and Avinash Srinivasan, USA
    • A Peer to Peer trust management system based on Disposition to Trust, Rachid Saadi, Jean-Marc Pierson and Lionel Brunie, France
  • ACM SAC TRECK 2011
    • "Hierarchical Trust Management for Wireless Sensor Networks and Its Application to Trust-Based Routing", Fenye Bao, Ing-Ray Chen, Moonjeong Chang and Jin-Hee Cho
    • "ImageAlert: Credibility Analysis of Text-image Pairs on the Web", Yusuke Yamamoto and Katsumi Tanaka
    • "Verification of Data Pattern for Interactive Privacy Preservation Model", Sujoy Ray, Mohammad Fahim Nizam, Swagata Das and Benjamin C. M. Fung
    • "A formal approach towards measuring trust in distributed systems", Guido Schryen, Melanie Volkamer and Sebastian Ries
  • ACM SAC TRECK 2012
    • "The Relation between User Intervention and User Satisfaction for Information Recommendation", Yoshinori Hijikata, Yuki Kai and Shogo Nishida
    • "Using Inferred Tag Ratings to Improve User-based Collaborative Filtering", Qi Qi, Zhenyu Chen, Jia Liu and Chengfeng Hui
    • "Preference-Oriented QoS-based Service Discovery with Dynamic Trust and Reputation Management", Zeinab Noorian, Michael Fleming and Stephen Marsh
    • "An Adaptive and Socially-Compliant Trust Management System for Virtual Communities", Reda Yaich, Olivier Boissier, Philippe Jaillon and Gauthier Picard
    • "Subjective Review-based Reputation", Gianpiero Costantino, Charles Morisset and Marinella Petrocchi
    • "Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users", Alan Said and Brijnesh-Johannes Jain
  • ACM SAC TRECK 2013
    • Credible Recommendation Exchange Mechanism for P2P Reputation Systems, Eleni Koutrouli and Aphrodite Tsalgatidou
    • Estimating Domain-based User Influence in Social Networks, Mario Cataldi, Nupur Mittal and Marie-Aude Aufaure
    • Composite Trust-based Public Key Management in Mobile Ad Hoc Networks, Jin-Hee Cho, Kevin Chan and Ing-Ray Chen
    • Zigzag: Partial Mutual Revocation Based Trust Management in Ad Hoc Networks, Xin Chen, Harshal Patankar, Sencun Zhu, Mudhakar Srivatsa and Jeff Opper
    • A Framework for Evaluating Trust of Service Providers in Cloud Marketplaces, Sheikh Mahbub Habib, Vijay Varadharajan and Max Mühlhäuser
  • ACM SAC TRECK 2014
    • Privacy Preserving Trusted Social Feedback, Anirban Basu, KDDI R&D Laboratories, Inc., Japan, Juan Camilo Corena, KDDI R&D Laboratories, Inc., Japan, Shinsaku Kiyomoto, KDDI R&D Laboratories, Inc., Japan, Stephen Marsh, University of Ontario Institute of Technology, Canada, Jaideep Vaidya, Rutgers, The State University of New Jersey, USA, Guibing Guo, Nanyang Technological University, Singapore, Jie Zhang, Nanyang Technological University, Singapore, Yutaka Miyake, KDDI R&D Laboratories, Inc., Japan
    • Privacy-Preserving Reputation Management, Ronald Petrlic, University of Paderborn, Germany, Sascha Lutters, University of Paderborn, Germany, Christoph Sorge, University of Paderborn, Germany
    • Case Study: Legal Requirements for the use of Social Login Features for Online Reputation Updates, Yung Shin Van Der Sype, ICRI – KU Leuven, Belgium, Jean-Marc Seigneur, Université de Genève, Switzerland
    • MUSES: A Corporate User-Centric System which Applies Computational Intelligence Methods, A.M. Mora, University of Granada, Spain, P. De las Cuevas, University of Granada, Spain, J.J. Merelo, University of Granada, Spain, S. Zamarripa, S2 Grupo, Spain, M. Juan, S2 Grupo, Spain, A.I. Esparcia-Alcázar, S2 Grupo, Spain, M. Burvall, Sweden Connectivity, Sweden, H. Arfwedson, Sweden Connectivity, Sweden, Z. Hodaie, University of Hamburg, Germany
    • Learning Reputation in an Authorship Network, Charanpal Dhanjal, Télécom ParisTech, France, Stéphan Clémençon, Télécom ParisTech, France
  • ACM SAC TRECK 2015
    • An Algorithm for Distributed Certificate Chain Discovery in Open Environments, Samane Abdi and John Herbert
    • A Security Configuration Assessment for Android Devices, Daniel Vecchiato, Marco Vieira and Eliane Martins 
    • A modeling approach for credit card fraud detection in electronic payment services, Gabriel Preti, Adriano Pereira and Roberto Hirata
    • Bi-directional Trust Index Computation in Resource Marketplace, Avinash Sharma, Tridib Mukherjee, Partha Dutta and Vinay Hegde
    • Trust-based Collection of Information in Distributed Reputation Networks, Dimitra Gkorou, Johan Pouwelse and Dick Epema
    • A Reputation System supporting Unlinkable, yet Authorized Expert Ratings, Andreas Kokoschka, Ronald Petrlic and Christoph Sorge
  • ACM SAC TRECK 2016
    • Defining Measurements for Analyzing Information Security Risk Reports in the Telecommunications Sector, Yves Le Bray, Nicolas Mayer, Jocelyn Aubert
    • Solving Sybil Attacks Using Evolutionary Game Theory, Farah Saab, Ayman Kayssi, Imad Elhajj, Ali Chehab
    • Feasibility Study of Context-Awareness Device Comfort Calculation Methods and Their Application to Comfort-Based Access Control, Jingjing Guo, Christian Damsgaard Jensen, Jianfeng Ma