Modelling and simulation framework for reactive transport of organic contaminants in bed-sediments using a pure java object - oriented paradigm

Numerical modelling and simulation of organic contaminant reactive transport in the environment is being increasingly relied upon for a wide range of tasks associated with risk-based decision-making, such as prediction of contaminant profiles, optimisation of remediation methods, and monitoring of changes resulting from an implemented remediation scheme. The lack of integration of multiple mechanistic models to a single modelling framework, however, has prevented the field of reactive transport modelling in bed-sediments from developing a cohesive understanding of contaminant fate and behaviour in the aquatic sediment environment. This paper will investigate the problems involved in the model integration process, discuss modelling and software development approaches, and present preliminary results from use of CORETRANS, a predictive modelling framework that simulates 1-dimensional organic contaminant reaction and transport in bed-sediments

Proceedings of International Workshop "Global Computing: Programming Environments, Languages, Security and Analysis of Systems"

According to the IST/ FET proactive initiative on GLOBAL COMPUTING, the goal is to obtain techniques (models, frameworks, methods, algorithms) for constructing systems that are flexible, dependable, secure, robust and efficient. The dominant concerns are not those of representing and manipulating data efficiently but rather those of handling the co-ordination and interaction, security, reliability, robustness, failure modes, and control of risk of the entities in the system and the overall design, description and performance of the system itself. Completely different paradigms of computer science may have to be developed to tackle these issues effectively. The research should concentrate on systems having the following characteristics: • The systems are composed of autonomous computational entities where activity is not centrally controlled, either because global control is impossible or impractical, or because the entities are created or controlled by different owners. • The computational entities are mobile, due to the movement of the physical platforms or by movement of the entity from one platform to another. • The configuration varies over time. For instance, the system is open to the introduction of new computational entities and likewise their deletion. The behaviour of the entities may vary over time. • The systems operate with incomplete information about the environment. For instance, information becomes rapidly out of date and mobility requires information about the environment to be discovered. The ultimate goal of the research action is to provide a solid scientific foundation for the design of such systems, and to lay the groundwork for achieving effective principles for building and analysing such systems. This workshop covers the aspects related to languages and programming environments as well as analysis of systems and resources involving 9 projects (AGILE , DART, DEGAS , MIKADO, MRG, MYTHS, PEPITO, PROFUNDIS, SECURE) out of the 13 founded under the initiative. After an year from the start of the projects, the goal of the workshop is to fix the state of the art on the topics covered by the two clusters related to programming environments and analysis of systems as well as to devise strategies and new ideas to profitably continue the research effort towards the overall objective of the initiative. We acknowledge the Dipartimento di Informatica and Tlc of the University of Trento, the Comune di Rovereto, the project DEGAS for partially funding the event and the Events and Meetings Office of the University of Trento for the valuable collaboration

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively

No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone

It is generally recognized that the traffic generated by an individual connected to a network acts as his biometric signature. Several tools exploit this fact to fingerprint and monitor users. Often, though, these tools assume to access the entire traffic, including IP addresses and payloads. This is not feasible on the grounds that both performance and privacy would be negatively affected. In reality, most ISPs convert user traffic into NetFlow records for a concise representation that does not include, for instance, any payloads. More importantly, large and distributed networks are usually NAT'd, thus a few IP addresses may be associated to thousands of users. We devised a new fingerprinting framework that overcomes these hurdles. Our system is able to analyze a huge amount of network traffic represented as NetFlows, with the intent to track people. It does so by accurately inferring when users are connected to the network and which IP addresses they are using, even though thousands of users are hidden behind NAT. Our prototype implementation was deployed and tested within an existing large metropolitan WiFi network serving about 200,000 users, with an average load of more than 1,000 users simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned out to be very effective, with an accuracy greater than 90%. We also devised new tools and refined existing ones that may be applied to other contexts related to NetFlow analysis