To reduce memory pressure, modern operating systems and hypervisors such as Linux/KVM deploy page-level memory fusion to merge physical memory pages with the same content (i.e., page fusion). A write to a fused memory page triggers a copy-on-write event that unmerges the page to preserve correct semantics. While page fusion is crucial in saving memory in production, recent work shows significant security weaknesses in its current implementations. Attackers can abuse timing side channels on the unmerge operation to leak sensitive data such as randomized pointers. Additionally, they can exploit the predictability of the merge operation to massage physical memory for reliable Rowhammer attacks. In this paper, we present VUsion, a secure page fusion system. VUsion can stop all the existing and even new classes of attack, where attackers leak information by side-channeling the merge operation or massage physical memory via predictable memory reuse patterns. To mitigate information disclosure attacks, we ensure attackers can no longer distinguish between fused and non-fused pages. To mitigate memory massaging attacks, we ensure fused pages are always allocated from a high-entropy pool. Despite its secure design, our comprehensive evaluation shows that VUsion retains most of the memory saving benefits of traditional memory fusion with negligible performance overhead while maintaining compatibility with other advanced memory management features.

Predicting the output power of renewable energy production plants distributed on a wide territory is a really valuable goal, both for marketing and energy management purposes. Vi-POC (Virtual Power Operating Center) project aims at designing and implementing a prototype which is able to achieve this goal. Due to the heterogeneity and the high volume of data, it is necessary to exploit suitable Big Data analysis techniques in order to perform a quick and secure access to data that cannot be obtained with traditional approaches for data management. In this paper, we describe Vi-POC -- a distributed system for storing huge amounts of data, gathered from energy production plants and weather prediction services. We use HBase over Hadoop framework on a cluster of commodity servers in order to provide a system that can be used as a basis for running machine learning algorithms. Indeed, we perform one-day ahead forecast of PV energy production based on Artificial Neural Networks in two learning settings, that is, structured and non-structured output prediction. Preliminary experimental results confirm the validity of the approach, also when compared with a baseline approach.