Biblio

Differential privacy has emerged as a promising mechanism for privacy-safe data mining. One popular differential privacy mechanism allows researchers to pose queries over a dataset, and adds random noise to all output points to protect privacy. While differential privacy produces useful data in many scenarios, added noise may jeopardize utility for queries posed over small populations or over long-tailed datasets. Gehrke et al. proposed crowd-blending privacy, with random noise added only to those output points where fewer than k individuals (a configurable parameter) contribute to the point in the same manner. This approach has a lower privacy guarantee, but preserves more research utility than differential privacy. We propose an even more liberal privacy goal—commoner privacy—which fuzzes (omits, aggregates or adds noise to) only those output points where an individual's contribution to this point is an outlier. By hiding outliers, our mechanism hides the presence or absence of an individual in a dataset. We propose one mechanism that achieves commoner privacy—interactive k-anonymity. We also discuss query composition and show how we can guarantee privacy via either a pre-sampling step or via query introspection. We implement interactive k-anonymity and query introspection in a system called Patrol for network trace processing. Our evaluation shows that commoner privacy prevents common attacks while preserving orders of magnitude higher research utility than differential privacy, and at least 9-49 times the utility of crowd-blending privacy.

In the cyber crime huge log data, transactional data occurs which tends to plenty of data for storage and analyze them. It is difficult for forensic investigators to play plenty of time to find out clue and analyze those data. In network forensic analysis involves network traces and detection of attacks. The trace involves an Intrusion Detection System and firewall logs, logs generated by network services and applications, packet captures by sniffers. In network lots of data is generated in every event of action, so it is difficult for forensic investigators to find out clue and analyzing those data. In network forensics is deals with analysis, monitoring, capturing, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper focuses on data collection from the cyber system and web browser. The FTK 4.0 is discussing for memory forensic analysis and remote system forensic which is to be used as evidence for aiding investigation.