No Arabic abstract
In the current world of economic crises, the cost control is one of the chief concerns for all types of industries, especially for the small venders. The small vendors are suppose to minimize their budget on Information Technology by reducing the initial investment in hardware and costly database servers like ORACLE, SQL Server, SYBASE, etc. for the purpose of data processing and storing. In other divisions, the electronic devices manufacturing companies want to increase the demand and reduce the manufacturing cost by introducing the low cost technologies. The new small devices like ipods, iphones, palm top etc. are now-a-days used as data computation and storing tools. For both the cases mentioned above, instead of going for the costly database servers which additionally requires extra hardware as well as the extra expenses in training and handling, the flat file may be considered as a candidate due to its easy handling nature, fast accessing, and of course free of cost. But the main hurdle is the security aspects which are not up to the optimum level. In this paper, we propose a methodology that combines all the merit of the flat file and with the help of a novel steganographic technique we can maintain the utmost security fence. The new proposed methodology will undoubtedly be highly beneficial for small vendors as well as for the above said electronic devices manufacturer
Digital multimedia watermarking technology was suggested in the last decade to embed copyright information in digital objects such images, audio and video. However, the increasing use of relational database systems in many real-life applications created an ever increasing need for watermarking database systems. As a result, watermarking relational database systems is now merging as a research area that deals with the legal issue of copyright protection of database systems. Approach: In this study, we proposed an efficient database watermarking algorithm based on inserting binary image watermarks in non-numeric mutli-word attributes of selected database tuples. Results: The algorithm is robust as it resists attempts to remove or degrade the embedded watermark and it is blind as it does not require the original database in order to extract the embedded watermark. Conclusion: Experimental results demonstrated blindness and the robustness of the algorithm against common database attacks.
Databases in the past have helped businesses maintain and extract insights from their data. Today, it is common for a business to involve multiple independent, distrustful parties. This trend towards decentralization introduces a new and important requirement to databases: the integrity of the data, the history, and the execution must be protected. In other words, there is a need for a new class of database systems whose integrity can be verified (or verifiable databases). In this paper, we identify the requirements and the design challenges of verifiable databases.We observe that the main challenges come from the need to balance data immutability, tamper evidence, and performance. We first consider approaches that extend existing OLTP and OLAP systems with support for verification. We next examine a clean-slate approach, by describing a new system, Spitz, specifically designed for efficiently supporting immutable and tamper-evident transaction management. We conduct a preliminary performance study of both approaches against a baseline system, and provide insights on their performance.
A major algorithmic challenge in designing applications intended for secure remote execution is ensuring that they are oblivious to their inputs, in the sense that their memory access patterns do not leak sensitive information to the server. This problem is particularly relevant to cloud databases that wish to allow queries over the clients encrypted data. One of the major obstacles to such a goal is the join operator, which is non-trivial to implement obliviously without resorting to generic but inefficient solutions like Oblivious RAM (ORAM). We present an oblivious algorithm for equi-joins which (up to a logarithmic factor) matches the optimal $O(nlog n)$ complexity of the standard non-secure sort-merge join (on inputs producing $O(n)$ outputs). We do not use use expensive primitives like ORAM or rely on unrealistic hardware or security assumptions. Our approach, which is based on sorting networks and novel provably-oblivious constructions, is conceptually simple, easily verifiable, and very efficient in practice. Its data-independent algorithmic structure makes it secure in various different settings for remote computation, even in those that are known to be vulnerable to certain side-channel attacks (such as Intel SGX) or with strict requirements for low circuit complexity (like secure multiparty computation). We confirm that our approach is easily realizable through a compact implementation which matches our expectations for performance and is shown, both formally and empirically, to possess the desired security characteristics.
Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive technologies such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) have been used to detect motor imagery, disease, or mental state. It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals. The fusion algorithm for EEG and NIRS sources is the key to implement them in real-life applications. In this research, we propose three fusion methods for the hybrid of the EEG and NIRS-based brain-computer interface system: linear fusion, tensor fusion, and $p$th-order polynomial fusion. Firstly, our results prove that the hybrid BCI system is more accurate, as expected. Secondly, the $p$th-order polynomial fusion has the best classification results out of the three methods, and also shows improvements compared with previous studies. For a motion imagery task and a mental arithmetic task, the best detection accuracy in previous papers were 74.20% and 88.1%, whereas our accuracy achieved was 77.53% and 90.19% . Furthermore, unlike complex artificial neural network methods, our proposed methods are not as computationally demanding.
The insights revealed from process mining heavily rely on the quality of event logs. Activities extracted from healthcare information systems with the free-text nature may lead to inconsistent labels. Such inconsistency would then lead to redundancy of activity labels, which refer to labels that have different syntax but share the same behaviours. The identifications of these labels from data-driven process discovery are difficult and rely heavily on resource-intensive human review. Existing work achieves low accuracy either redundant activity labels are in low occurrence frequency or the existence of numerical data values as attributes in event logs. However, these phenomena are commonly observed in healthcare information systems. In this paper, we propose an approach to detect redundant activity labels using control-flow relations and numerical data values from event logs. Natural Language Processing is also integrated into our method to assess semantic similarity between labels, which provides users with additional insights. We have evaluated our approach through synthetic logs generated from the real-life Sepsis log and a case study using the MIMIC-III data set. The results demonstrate that our approach can successfully detect redundant activity labels. This approach can add value to the preprocessing step to generate more representative event logs for process mining tasks in the healthcare domain.