Privacy-Preserving Detection of Sensitive Data Exposure

Privacy-Preserving Detection of Sensitive Data Exposure

Abstract

Statistics from security firms, research institutions and government organizations show that the numbers of data-leak instances have grown rapidly in recent years. Among various data-leak cases, human mistakes are one of the main causes of data loss. There exist solutions detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. A common approach is to screen content in storage and transmission for exposed sensitive information. Such an approach usually requires the detection operation to be conducted in secrecy. However, this secrecy requirement is challenging to satisfy in practice, as detection servers may be compromised or outsourced.

In this paper, we present a privacy preserving data-leak detection (DLD) solution to solve the issue where a special set of sensitive data digests is used in detection. The advantage of our method is that it enables the data owner to safely delegate the detection operation to a semihonest provider without revealing the sensitive data to the provider. We describe how Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. The evaluation results show that our method can support accurate detection with very small number of false alarms under various data-leak scenarios. Privacy-Preserving Detection of Sensitive Data Exposure

HARDWARE REQUIREMENT:

• Speed       –    1 GHz
• Processor      –    Pentium –IV
• RAM       –    256 MB (min)
• Hard Disk      –   20 GB
• Floppy Drive       –    44 MB
• Key Board      –    Standard Windows Keyboard
• Mouse       –    Two or Three Button Mouse
• Monitor      –    SVGA

 SOFTWARE REQUIREMENTS:

• Operating System        :   Windows XP or Win7
• Front End                    :   Microsoft Visual Studio .NET 2008
• Script                         :    C# Script
• Back End                 :   MS-SQL Server 2005
• Document             :   MS-Office 2007

Existing System:

• Existing detecting and preventing data leaks requires a set of complementary solutions, which may include data-leak detection, data confinement, stealthy malware detection, and policy enforcement.
• Network data-leak detection (DLD) typically performs deep packet inspection (DPI) and searches for any occurrences of sensitive data patterns. DPI is a technique to analyze payloads of IP/TCP packets for inspecting application layer data, e.g., HTTP header/content.
• Alerts are triggered when the amount of sensitive data found in traffic passes a threshold. The detection system can be deployed on a router or integrated into existing network intrusion detection systems (NIDS).
• Straightforward realizations of data-leak detection require the plaintext sensitive data. However, this requirement is undesirable, as it may threaten the confidentiality of the sensitive information. If a detection system is compromised, then it may expose the plaintext sensitive data (in memory).
• In addition, the data owner may need to outsource the data-leak detection to providers, but may be unwilling to reveal the plaintext sensitive data to them. Therefore, one needs new data-leak detection solutions that allow the providers to scan content for leaks without learning the sensitive information.

Proposed System:

• We propose a data-leak detection solution which can be outsourced and be deployed in a semihonest detection environment. We design, implement, and evaluate our fuzzy fingerprint technique that enhances data privacy during data-leak detection operations.
• Our approach is based on a fast and practical one-way computation on the sensitive data (SSN records, classified documents, sensitive emails, etc.). It enables the data owner to securely delegate the content-inspection task to DLD providers without exposing the sensitive data.
• Our detection method, the DLD provider, who is modeled as an honest-but-curious (aka semi-honest) adversary, can only gain limited knowledge about the sensitive data from either the released digests, or the content being inspected. Using our techniques, an Internet service provider (ISP) can perform detection on its customers’ traffic securely and provide data-leak detection as an add-on service for its customers. In another scenario, individuals can mark their own sensitive data and ask the administrator of their local network to detect data leaks for them.
• Our detection procedure, the data owner computes a special set of digests or fingerprints from the sensitive data and then discloses only a small amount of them to the DLD provider. The DLD provider computes fingerprints from network traffic and identifies potential leaks in them.
• To prevent the DLD provider from gathering exact knowledge about the sensitive data, the collection of potential leaks is composed of real leaks and noises. It is the data owner, who post-processes the potential leaks sent back by the DLD provider and determines whether there is any real data leak.