Steganography

Multi Layer Security (MLS) is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the internet. For hiding secret information in images, there exists a large variety of techniques some are more complex than others and all of them have respective strong and weak points. Different applications may require absolute invisibility of the secret information, while others require a large secret message to be hidden. This project report intends to give an overview of image encryption, its uses and techniques. It also attempts to identify the requirements of a good algorithm and briefly reflects on which techniques are more suitable for applications

On the security of NoSQL cloud database services

Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a public cloud infrastructure where, the Cloud Service Provider (CSP) is not completely trusted by the data owner. We present a high level description of several architectures combining modern cryptographic primitives for achieving this goal. A novel searchable security scheme is proposed to leverage secure query processing in presence of a malicious cloud insider without disclosing sensitive information. A holistic database security scheme comprised of data confidentiality and information leakage prevention is proposed in this dissertation. The main contributions of our work are: (i) A searchable security scheme for non-relational databases of the cloud DBaaS; (ii) Leakage minimization in the untrusted cloud. The analysis of experiments that employ a set of established cryptographic techniques to protect databases and minimize information leakage, proves that the performance of the proposed solution is bounded by communication cost rather than by the cryptographic computational effort

Secured Data Masking Framework and Technique for Preserving Privacy in a Business Intelligence Analytics Platform

The main concept behind business intelligence (BI) is how to use integrated data across different business systems within an enterprise to make strategic decisions. It is difficult to map internal and external BI’s users to subsets of the enterprise’s data warehouse (DW), resulting that protecting the privacy of this data while maintaining its utility is a challenging task. Today, such DW systems constitute one of the most serious privacy breach threats that an enterprise might face when many internal users of different security levels have access to BI components. This thesis proposes a data masking framework (iMaskU: Identify, Map, Apply, Sign, Keep testing, Utilize) for a BI platform to protect the data at rest, preserve the data format, and maintain the data utility on-the-fly querying level. A new reversible data masking technique (COntent BAsed Data masking - COBAD) is developed as an implementation of iMaskU. The masking algorithm in COBAD is based on the statistical content of the extracted dataset, so that, the masked data cannot be linked with specific individuals or be re-identified by any means. The strength of the re-identification risk factor for the COBAD technique has been computed using a supercomputer where, three security scheme/attacking methods are considered, a) the brute force attack, needs, on average, 55 years to crack the key of each record; b) the dictionary attack, needs 231 days to crack the same key for the entire extracted dataset (containing 50,000 records), c) a data linkage attack, the re-identification risk is very low when the common linked attributes are used. The performance validation of COBAD masking technique has been conducted. A database schema of 1GB is used in TPC-H decision support benchmark. The performance evaluation for the execution time of the selected TPC-H queries presented that the COBAD speed results are much better than AES128 and 3DES encryption. Theoretical and experimental results show that the proposed solution provides a reasonable trade-off between data security and the utility of re-identified data

A novel architecture for secure database processing in cloud computing

Security, particularly data privacy, is one of the biggest barriers to the adoption of Database-as-a-Service (DBaaS) in Cloud Computing. Recent security breaches demonstrate that a more powerful protection mechanism is needed to protect data confidentiality from any honest-but-curious administrator. Typical prior effort on addressing this security problem is either prohibitively slow or highly restrictive in operation. In this thesis, a novel cloud system architecture CypherDB, which makes use of a secure processor, is proposed to protect the confidentiality of outsourced database processing. To achieve this, a framework is developed to use these secure processors in the cloud for secure database processing. This framework allows distributed and parallel processing of the encrypted data and exhibits virtualization features in Cloud Computing. The CypherDB architecture also relies on two major components to protect the privacy of an outsourced database against any honest-but-curious administrator of high performance. Firstly, a novel database encryption scheme is developed to protect the outsourced database which can be executed under a CypherDB secure processor with high performance. Our proposed scheme makes use of custom instructions to hide the encryption latency from the program execution. This scheme is extensively validated through an integration with SQLite, a practical database application program. Secondly, a novel secure processor architecture is also developed to provide architectural support to our proposed database encryption scheme and efficient protection mechanism to secure all intermediate data generated on-the-fly during query execution. The efficiency, robustness and the cost of our novel processor architecture are validated and evaluated through extensive simulations and implementation on a FPGA platform. A fully-functional Field-Programmable Gate Array (FPGA) implementation of our CypherDB secure processor and simulation studies demonstrate that our proposed architecture is cost-effective and of high performance. Our experiment of running the TPC-H database benchmark on SQLite demonstrates 10 to 14 percent performance overhead on average. The security components in CypherDB consume about 21K Logic Elements and 54 Block RAMs on the FPGA. The modification of SQLite only consists of 208 lines of code (LOC).Open Acces

A platform for discovering and sharing confidential ballistic crime data.

Criminal investigations generate large volumes of complex data that detectives have to analyse and understand. This data tends to be "siloed" within individual jurisdictions and re-using it in other investigations can be difficult. Investigations into trans-national crimes are hampered by the problem of discovering relevant data held by agencies in other countries and of sharing those data. Gun-crimes are one major type of incident that showcases this: guns are easily moved across borders and used in multiple crimes but finding that a weapon was used elsewhere in Europe is difficult. In this paper we report on the Odyssey Project, an EU-funded initiative to mine, manipulate and share data about weapons and crimes. The project demonstrates the automatic combining of data from disparate repositories for cross-correlation and automated analysis. The data arrive from different cultural/domains with multiple reference models using real-time data feeds and historical databases

EXPLORING CONFIDENTIALITY AND PRIVACY OF IMAGE IN CLOUD COMPUTING

With the increasing popularity of cloud computing, clients are storing their data in cloud servers and are using “software as a service” for computing services. However, clients’ data may be sensitive, critical, and private, and processing such data with cloud servers may result in losing data privacy or compromising data confidentiality. Some cloud servers may be dishonest, while malicious entities may compromise others. In order to protect data privacy and confidentiality, clients need to be able to hide their actual data values and send the obfuscated values to cloud servers. This thesis deals with the outsourcing of computing to cloud servers, in which clients’ images can be computed and stored. This thesis proposes a technique that obfuscates images before sending them to servers, so these servers can perform computations on images without knowing the actual images. The proposed technique is expected to ensure data privacy and confidentiality. Servers will not be able to identify an individual whose images are stored and manipulated by the server. In addition, our approach employs an obfuscating technique to maintain the confidentiality of images, allowing cloud servers to compute obfuscated data accurately without knowing the actual data value, thus supporting privacy and confidentiality. The proposed approach is based on the Rabin block cipher technique, which has some weaknesses, however. The main drawback is its decryption technique, which results in four values, and only one of these values represents the actual value of plain data. Another issue is that the blocking technique requires a private key for each block that requires a high-computing effort; requiring one private key for each block of data demands that a great number of keys be stored by the client. As a result, it decreases the robustness of the Rabin block cipher. This thesis proposes additional techniques to overcome some of the weaknesses of the Rabin block cipher by introducing some new features, such as tokenization, a digit counter, and a set of blocks. The new technique increases the privacy of data and decreases the computational complexity by requiring fewer private keys. The new features have been implemented in image processing in order to demonstrate their applicability. However, in order to apply our approach to images, we must first apply some preprocessing techniques on images to make them applicable to being obfuscated by our proposed obfuscating system

Data Profiling in Cloud Migration: Data Quality Measures while Migrating Data from a Data Warehouse to the Google Cloud Platform

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn today times, corporations have gained a vast interest in data. More and more, companies realized that the key to improving their efficiency and effectiveness and understanding their customers’ needs and preferences better was reachable by mining data. However, as the amount of data grow, so must the companies necessities for storage capacity and ensuring data quality for more accurate insights. As such, new data storage methods must be considered, evolving from old ones, still keeping data integrity. Migrating a company’s data from an old method like a Data Warehouse to a new one, Google Cloud Platform is an elaborate task. Even more so when data quality needs to be assured and sensible data, like Personal Identifiable Information, needs to be anonymized in a Cloud computing environment. To ensure these points, profiling data, before or after it migrated, has a significant value by design a profile for the data available in each data source (e.g., Databases, files, and others) based on statistics, metadata information, and pattern rules. Thus, ensuring data quality is within reasonable standards through statistics metrics, and all Personal Identifiable Information is identified and anonymized accordingly. This work will reflect the required process of how profiling Data Warehouse data can improve data quality to better migrate to the Cloud

A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

International audienceThe medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to Grid middlewares and of preserving patients' privacy in a wide and distributed multi-user system. These constraints are often limiting the use of Grids for manipulating sensitive medical data. This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protection through strict data access control, anonymization and encryption. The multi-level access control provides the flexibility needed for imple! menting complex medical use-cases. Data anonymization prevents the exposure of most sensitive data to unauthorized users, and data encryption guarantees data protection even when it is stored at remote sites. Moreover, the developed prototype provides a Grid storage resource manager (SRM) interface to standard medical DICOM servers thereby enabling transparent access to medical data without interfering with medical practice