In addition to the ACM SAC TRECK track, the trustcomp community has organized 3 editions of the IEEE SECURECOMM SECOVAL workshop from 2005 to 2007. SECOVAL meant the value of security through collaboration and the outcomes of the 3 workshops have been summarized in this book:
Security is usually centrally managed, for example in a form of policies duly executed by individual nodes. The SECOVAL workshop covers the alternative trend of using collaboration and trust to provide security. Instead of centrally managed security policies, nodes may use specific knowledge (both local and acquired from other nodes) to make security-related decisions. For example, in reputation-based schemes, the reputation of a given node (and hence its security access rights) can be determined based on the recommendations of peer nodes. As systems are being deployed on ever-greater scale without direct connection to their distant home base, the need for selfmanagement is rapidly increasing. Interaction after interaction, as the nodes collaborate, there is the emergence of a digital ecosystem. By guiding the local decisions of the nodes, for example, with whom the nodes collaborate, global properties of the ecosystem where the nodes operate may be guaranteed. Thus, the security property of the ecosystem may be driven by self-organizing mechanisms. Depending on which local collaboration is preferred, a more trustworthy ecosystem may emerge.
Any useful collaboration is at some point sharing data.Unfortunately, data sharing is one of the greatest hurdles getting in the way of otherwise beneficial collaborations. Data regarding one’s security stance is particularly sensitive, often indicating ones own security weaknesses. This data could include computer or network logs of security incidents, architecture documents, or sensitive organizational information. Even when the data may not compromise the data owner’s security stance, sharing may violate a customer’s privacy. Data sanitization techniques such as anonymization and other mechanisms such as privacy-preserving data mining and statistical data mining try to address this tension between the need to share information and protect sensitive information and user privacy.
While papers will be considered that address any of the topics of security through collaboration from previous years (e.g., benefits from collaboration, methods of creating or measuring trust, self-organizing coalitions and risk analysis), the focus of the workshop will be around privacy and data sanitization. This topic is further divided into three main areas, each answering the related research questions. Contributions addressing at least one of these areas are more likely to be accepted.
What are the fundamental issues that need to be addressed in the areas of data sanitization and anonymization? What problems must be solved to make current tools more effective and sharing more wide-spread? One thing fundamentally missing from this area are metrics to help evaluate the trade-off between information loss and security/privacy. Metrics are needed to measure information loss, and they are needed to measure the utility of the computer log or data source after anonymization. A classification of the types of attacks on anonymization schemes and a formal adversarial model is lacking. Such a threat model would help to develop metrics of the security provided by an anonymization scheme. Are there other basic anonymization algorithms needed for special types of data? One of the last new algorithms developed was prefix-preserving anonymization for IP addresses. Sometimes anonymization needs to be reversed once a problem has been found. How can this be done and when is it practical? Finally, data injection attacks (e.g., an adversary can inject events into a system knowing they will appear in a later public release of anonymized data) are particularly to difficult to protect against. What methods can be used to mitigate such attacks on anonymization systems.
What are the practical problems that have yet to be addressed by current anonymization systems? What new tools and frameworks exist for the task? What are the economic implications of data sanitization and preserving privacy? What are the legal issues involved in protecting privacy, and how do they differ by geo-political areas? How can anonymization utilities be made more usable by a wider audience, and who are the potential consumers? Interesting case studies of implementations of anonymization and privacy enhancing technologies will be considered. Of particular interest are case studies by industry of how they have addressed these hurdles to data sharing. How can effective policies be created and negotiated? Do we need a common anonymization policy language, and what would we need in such a language? How can we identify sensitive information especially in the context of multiple data sources? What are some best practice guidelines that one can follow before releasing or sharing sensitive data? Finally special issues surrounding real-time anonymization and anonymity in Peer-to-Peer systems is of interest.
What privacy and data sanitization issues are specific to data bases and data mining? This would include traditional topics on privacy-preserving data mining and statistical databases. It would also include topics on inference attacks and data aggregation. Much of the research in this area has focused on privacy preserving transformations that would minimally alter traditional data mining functions (e.g., link analysis and clustering). Submissions focusing on less traditional data mining functions are especially encouraged. Another problem with anonymization is data mining across sets anonymized by different parties in different ways. New methods should be created for collaborative anonymization that makes mappings consistent between contributing parties but irreversible to all.