Overcoming Free Riding in Multi-Party Computations

This paper addresses the question of multi party computation in a model with asymmetric information. Each agent has a private value (secret), but in contrast to standard models, the agent incurs a cost when retrieving the secret. There is a social choice function the agents would like to compute and implement. All agents would like to perform a joint computation, which input is their vector of secrets. However, agents would like to free-ride on others contribution. A mechanism which elicits players secrets and performs the desired computation defines a game. A mechanism is `appropriate if it (weakly) implements the social choice function for all secret vectors. namely, if there exists an equilibrium in which it is able to elicit (sufficiently many) agents secrets and perform the computation, for all possible secret vectors. We show that `appropriate mechanisms approach agents sequentially and that they have low communication complexity

The Effect of a Blockchain-Supported, Privacy-Preserving System on Disclosure of Personal Data

In light of digitalization, customers increasingly share private data through their online behaviors and actions. Yet, customers have become reluctant to share data due to privacy concerns. From a psychological perspective, a reduction of users' perceived risks should result in a higher willingness to share sensitive data. The development of blockchain-supported, multi-part computation thereby represents an interesting novel empirical context to study such willingness to disclose personal data, as such technologies involve a privacy-preserving approach that could not only technically solve privacy issues but also ought to address precisely the user’s risk perception. Therefore, we conducted an online experiment with 420 participants to examine the willingness to disclose personal data dependent on different privacy protection mechanisms. A deception based experiment allowed to measure not only user intention, but also real user behavior. Surprisingly, our results demonstrate that participants shared similar amounts of personal data for blockchain-supported approaches and standard privacy policies. Even though an aversion to the blockchain system due to its novelty and potentially perceived complexity was not detected. Furthermore, we found that the willingness to share data increased significantly specifically for technically affine people when they were presented with the opportunity to monetize their data. We further discuss the effects of privacy awareness and whether prior knowledge of blockchain technology had a supporting effect for user acceptance

PSBD 2014: Overview of the 1^st international workshop on privacy and security of Big Data

The 1st International Workshop on Privacy and Security of Big Data (PSBD 2014) focuses the attention on privacy and security research issues in the context of Big Data, a vibrant and challenging research context which is playing a leading role in the Database research community. Indeed, while Big Data is gaining the attention from the research community, also driven by some relevant technological innovations (like Clouds) as well as novel paradigms (like social networks), the issues of privacy and security of Big Data represent a fundamental problem in this research context, due to the fact Big Data are typically published online for supporting knowledge management and fruition processes and, in addition to this, such data are usually handled by multiple owners, with possible secure multi-part computation issues. Some of the hot topics in the context privacy and security of Big Data include: (i) privacy and security of Big Data integration and exchange; (ii) privacy and security of Big Data in data-intensive Cloud computing; (iii) system architectures in support of privacy and security of Big Data, e.g., GPUs: (iv) privacy and security issues of Big Data querying and analysis. These topics are first-class aspects to be addressed and investigated by PSBD 2014. These proceedings contain the papers selected for presentation at the workshop. We received 12 submissions from countries in North America, Europe and Asia. After careful review, the program committee selected 5 papers for presentation at the workshop. The accepted papers were presented in 2 sessions: scalable privacy-preserving and security-control methods for Big Data processing, user-oriented and data-oriented privacy methods for Big Data processing. A panel discussed advanced aspects of privacy and security of Big Data. We hope that these proceedings will serve as a valuable reference for researchers and practitioners focusing on privacy and security of Big Data