Arogyasree: An Enhanced Grid-Based Approach to Mobile Telemedicine

A typical telemedicine system involves a small set of hospitals providing remote healthcare services to a small section of the society using dedicated nodal centers. However, in developing nations like India where majority live in rural areas that lack specialist care, we envision the need for much larger Internet-based telemedicine systems that would enable a large pool of doctors and hospitals to collectively provide healthcare services to entire populations. We propose a scalable, Internet-based P2P architecture for telemedicine integrating multiple hospitals, mobile medical specialists, and rural mobile units. This system, based on the store and forward model, features a distributed context-aware scheduler for providing timely and location-aware telemedicine services. Other features like zone-based overlay structure and persistent object space abstraction make the system efficient and easy to use. Lastly, the system uses the existing internet infrastructure and supports mobility at doctor and patient ends

Two-layered architecture for peer-to-peer concept search

The current search landscape consists of a small number of centralized search engines posing serious issues including centralized control, resource scalability, power consumption and inability to handle long tail of user interests. Since, the major search engines use syntactic search techniques, the quality of search results are also low, as the meanings of words are not considered effectively. A collaboratively managed peer-to-peer semantic search engine realized using the edge nodes of the internet could address most of the issues mentioned. We identify the issues related to knowledge management, word-to-concept mapping and efficiency in realizing a peer-to-peer concept search engine, which extends syntactic search with background knowledge of peers and searches based on concepts rather than words. We propose a two-layered architecture for peer-to-peer concept search to address the identified issues. In the two-layered approach, peers are organized into communities and background knowledge and document index are maintained at two levels. Universal knowledge is used to identify the appropriate communities for a query and search within the communities proceed based on the background knowledge developed independently by the communities. We developed proof-of-concept implementations of peer-to-peer syntactic search, straightforward single-layered and the proposed two-layered peer-to-peer concept search approaches. Our evaluation concludes that the proposed two-layered approach improves the quality and network efficiency substantially compared to a straightforward single-layered approach

Keep the PokerFace on! Thwarting cache side channel attacks by memory bus monitoring and cache obfuscation

Abstract Cloud instances are vulnerable to cross-core, cross-VM attacks against the shared, inclusive last-level cache. Automated cache template attacks, in particular, are very powerful as the vulnerabilities do not need to be manually identified. Such attacks can be devised using both the Prime+Probe and the Flush+Reload techniques. In this paper, we present PokerFace, a novel method to identify and mitigate such attacks. This approach allows us to identify suspicious cache accesses automatically, without prior knowledge about the system or access to hardware metrics. PokerFace consists of two components, Poker and Face. Poker executes a memory bus benchmark to measure the available bus bandwidth and derive information about cache accesses and possible side channel attacks. Our experiments with cache attacks show a reduction of up to 14% in the memory bandwidth during the attack. When an attack is detected, Poker triggers Face which performs cache obfuscation. We demonstrate the effectiveness of our approach against keypress logging attacks. We also test it against generic Prime+Probe and Flush+Reload attacks and show that it is practically useful against a variety of cache timing attacks. PokerFace incurs modest overheads (< 8%) and moreover, does not require support from the cloud provider or changes to the hypervisor. Unlike previously proposed techniques, it can be implemented by cloud subscribers