Dual concentric core dispersion compensating fibre optimised for WDM application

A dual concentric core fibre exhibiting a chromatic dispersion coefficient around -350 ps/(nm km) is used in a dispersion compensating module designed for WDM application. The optimised length ratio is larger than 20 and the residual chromatic dispersion is measured at 0.4 ps/(nm km) in the C-band     

Very large effective area singlemode photonic bandgap fibre

A photonic bandgap fibre consisting of a 34 /spl mu/m diameter pure silica core surrounded by a periodic cladding has been modelled and characterised. It guides a robust HE/sub 11/ mode at 1.55 /spl mu/m with a 517 /spl mu/m/sup 2/ effective area and 0.4 dB/m propagation losses.     

Flattening filter based on cascaded dual concentric core fibres

The use of dual concentric core fibres to realise a flattening filter is reported. The power spectrum of an ASE source has been flattened in the C-band from 5.8 to 1.4 dB.     

Resisting structural re-identification in anonymized social networks

We identify privacy risks associated with releasing network datasets and provide an algorithm that mitigates those risks. A network dataset is a graph representing entities connected by edges representing relations such as friendship, communication or shared activity. Maintaining privacy when publishing a network dataset is uniquely challenging because an individual’s network context can be used to identify them even if other identifying information is removed. In this paper, we introduce a parameterized model of structural knowledge available to the adversary and quantify the success of attacks on individuals in anonymized networks. We show that the risks of these attacks vary based on network structure and size and provide theoretical results that explain the anonymity risk in random networks. We then propose a novel approach to anonymizing network data that models aggregate network structure and allows analysis to be performed by sampling from the model. The approach guarantees anonymity for entities in the network while allowing accurate estimates of a variety of network measures with relatively little bias.     

Auditing a database under retention policies

Auditing the changes to a database is critical for identifying malicious behavior, maintaining data quality, and improving system performance. But an accurate audit log is an historical record of the past that can also pose a serious threat to privacy. Policies that limit data retention conflict with the goal of accurate auditing, and data owners have to carefully balance the need for policy compliance with the goal of accurate auditing. In this paper, we provide a framework for auditing the changes to a database system while respecting data retention policies. Our framework includes an historical data model that supports flexible audit queries, along with a language for retention policies that can hide individual attribute values or remove entire tuples from the history. Under retention policies, the audit history is partially incomplete. Thus, audit queries on the protected history can include imprecise results. We propose two different models (a tuple-independent model and a tuple-correlated model) for formalizing the meaning of audit queries. We implement policy application and query answering efficiently in a standard relational system and characterize the cases where accurate auditing can be achieved under retention restrictions.