Jose M. Such

Exploiting temporal locality in drowsy cache policies

Technology projections indicate that static power will become a major concern in future generations of high-performance microprocessors. Caches represent a significant percentage of the overall microprocessor die area. Therefore, recent research has concentrated on the reduction of leakage current dissipated by caches. The variety of techniques to control current leakage can be classified as non-state preserving or state preserving. Non-state preserving techniques power off selected cache lines while state preserving place selected lines into a low-power state. Drowsy caches are a recently proposed state-preserving technique. In order to introduce low performance overhead, drowsy caches must be very selective on which cache lines are moved to a drowsy statePast research on cache organization has focused on how best to exploit the temporal locality present in the data stream. In this paper we propose a novel drowsy cache policy called Reuse Most Recently used On (RMRO), which makes use of reuse information to trade off performance versus energy consumption. Our proposal improves the hit ratio for drowsy lines by about 67%, while reducing the power consumption by about 11.7% (assuming 70nm technology) with respect to previously proposed drowsy cache policies.

Open Challenges in Relationship-Based Privacy Mechanisms for Social Network Services

Social networking services (SNSs) such as Facebook and Twitter have experienced explosive growth during the few past years. Millions of users have created their profiles on these services because they experience great benefits in terms of friendship. SNSs can help people to maintain their friendships, organize their social lives, start new friendships, and meet others who share their hobbies and interests. However, all these benefits can be eclipsed by the privacy hazards that affect people in SNSs. People expose intimate information about their lives on SNSs, and this information affects the way others think about them. It is crucial that users be able to control how their information is distributed through the SNSs and decide who can access it. This article presents a list of privacy threats that can affect SNS users and what requirements privacy mechanisms should fulfill to prevent this threats. Then the article reviews current approaches and analyzes to what extent they cover the requirements.

A survey of privacy in multi-agent systems

Privacy has been a concern for humans long before the explosive growth of the Internet. The advances in information technologies have further increased these concerns. This is because the increasing power and sophistication of computer applications offers both tremendous opportunities for individuals, but also significant threats to personal privacy. Autonomous agents and multi-agent systems are examples of the level of sophistication of computer applications. Autonomous agents usually encapsulate personal information describing their principals, and therefore they play a crucial role in preserving privacy. Moreover, autonomous agents themselves can be used to increase the privacy of computer applications by taking advantage of the intrinsic features they provide, such as artificial intelligence, pro-activeness, autonomy, and the like. This article introduces the problem of preserving privacy in computer applications and its relation to autonomous agents and multi-agent systems. It also surveys privacy-related studies in the field of multi-agent systems and identifies open challenges to be addressed by future research.

Performance evaluation of open-source multiagent platforms

Nowadays, most multiagent platforms are internally designed as middleware and are usually implemented in Java and run on top of an operating system. This kind of design maximizes portability and reduces the development cost; however, it may lead to low performance and scalability. In this context, our research has the long-term goal of integrating into the operating system some key services which are currently supported by middleware platforms. The first step in achieving this goal is to study some well-known, open-source platforms in order to understand to what extent the internal design of a platform influences its performance.

Partial identities as a foundation for trust and reputation

This paper explores the relationships between the hard security concepts of identity and privacy on the one hand, and the soft security concepts of trust and reputation on the other hand. We specifically focus on two vulnerabilities that current trust and reputation systems have: the change of identity and multiple identities problems. As a result, we provide a privacy preserving solution to these vulnerabilities which integrates the explored relationships among identity, privacy, trust and reputation. We also provide a prototype of our solution to these vulnerabilities and an application scenario.

BFF: A tool for eliciting tie strength and user communities in social networking services

The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users’ relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.

Privacy Policy Negotiation in Social Media

Social media involve many shared items, such as photos, which may concern more than one user. The challenge is that users’ individual privacy preferences for the same item may conflict, so an approach that simply merges in some way the users’ privacy preferences may provide unsatisfactory results. Previous proposals to deal with the problem were either time-consuming or did not consider compromises to solve these conflicts (e.g., by considering unilaterally imposed approaches only). We propose a negotiation mechanism for users to agree on a compromise for these conflicts. The second challenge we address in this article relates to the exponential complexity of such a negotiation mechanism. To address this, we propose heuristics that reduce the complexity of the negotiation mechanism and show how substantial benefits can be derived from the use of these heuristics through extensive experimental evaluation that compares the performance of the negotiation mechanism with and without these heuristics. Moreover, we show that one such heuristic makes the negotiation mechanism produce results fast enough to be used in actual social media infrastructures with near-optimal results.

A performance evaluation of three multiagent platforms

In the last few years, many researchers have focused on testing the performance of Multiagent Platforms. Results obtained show a lack of performance and scalability on current Multiagent Platforms, but the existing research does not tackle poor efficiency causes. This article is aimed not only at testing the performance of Multiagent Platforms but also the discovery of Multiagent Platform design decisions that can lead to these deficiencies. Therefore, we are able to understand to what extent the internal design of a Multiagent Platform affects its performance. The experiments performed are focused on the features involved in agent communication.

Implicit Contextual Integrity in Online Social Networks

Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks and preventing them from exchanging inappropriate information in those contexts or disseminating sensitive information from some contexts to others. Contextual integrity is a privacy theory that conceptualises the appropriateness of information sharing based on the contexts in which this information is to be shared. Computational models of Contextual Integrity assume the existence of well-defined contexts, in which individuals enact pre-defined roles and information sharing is governed by an explicit set of norms. However, contexts in Online Social Networks are known to be implicit, unknown a priori and ever changing; users relationships are constantly evolving; and the information sharing norms are implicit. This makes current Contextual Integrity models not suitable for Online Social Networks.In this paper, we propose the first computational model of Implicit Contextual Integrity, presenting an information model for Implicit Contextual Integrity as well as a so-called Information Assistant Agent that uses the information model to learn implicit contexts, relationships and the information sharing norms in order to help users avoid inappropriate information exchanges and undesired information disseminations. Through an experimental evaluation, we validate the properties of the model proposed. In particular, Information Assistant Agents are shown to: (i) infer the information sharing norms even if a small proportion of the users follow the norms and in presence of malicious users; (ii) help reduce the exchange of inappropriate information and the dissemination of sensitive information with only a partial view of the system and the information received and sent by their users; and (iii) minimise the burden to the users in terms of raising unnecessary alerts.

Strategies for avoiding preference profiling in agent-based e-commerce environments

Agent-based electronic commerce is known to offer many advantages to users. However, very few studies have been devoted to deal with privacy issues in this domain. Privacy is of great concern and preserving users’ privacy plays a crucial role to promote their trust in agent-based technologies. In this paper, we focus on preference profiling, which is a well-known threat to users’ privacy. Specifically, we review strategies for customers’ agents to prevent seller agents from obtaining accurate preference profiles of the former group by using data mining techniques. We experimentally show the efficacy of each of these strategies and discuss their suitability in different situations. Our experimental results show that customers can improve their privacy notably with these strategies.