Tracking and Repairing Damaged Databases Using Before Image Table

Xie, Meiyi; Zhu, Hong; Feng, Yaokai; Guan-rong, HU

doi:10.1109/fcst.2008.25

Cited by 16 publications

(7 citation statements)

References 7 publications

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“…In [33], approaches were offered by the authors for data recovery that is maliciously attacked through the addition of Before-Image Tables (BI Tables). These BI Tables cannot be modified by any user at any time and have values of all deleted and updated data items.…”

Section: Data Integrity In Fog Computingmentioning

confidence: 99%

Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

et al. 2021

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The volume of data generated worldwide is rapidly growing. Cloud computing, fog computing, and the Internet of Things (IoT) technologies have been adapted to compute and process this high data volume. In coming years information technology will enable extensive developments in the field of healthcare and offer health care providers and patients broadened opportunities to enhance their healthcare experiences and services owing to heightened availability and enriched services through real-time data exchange. As promising as these technological innovations are, security issues such as data integrity and data consistency remain widely unaddressed. Therefore, it is important to engineer a solution to these issues. Developing a damage assessment and recovery control model for fog computing is critical. This paper proposes two models for using fog computing in healthcare: one for private fog computing distribution and one for public fog computing distribution. For each model, we propose a unique scheme to assess the damage caused by malicious attack, to accurately identify affected transactions and recover damaged data if needed. A transaction-dependency graph technique is used for both models to observe and monitor all transactions in the whole system. We conducted a simulation study to assess the applicability and efficacy of the proposed models. The evaluation rendered these models practicable and effective.

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Section: Data Integrity In Fog Computingmentioning

confidence: 99%

Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

et al. 2021

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“…The Before Images approach was suggested in [17]. The Before Image table is a table that is similar to the stored table in the database but without constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Tracking and Repairing Damaged Healthcare Databases Using the Matrix

Kaddoura

Haraty

Zekri

et al. 2015

International Journal of Distributed Sensor Networks

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In a distributed mobile e-health care system, e-health service providers exchange data on the fly in response to user queries without any centralized control. Local databases in e-health service providers might be intercepted during the exchange of data and read by intruders; and malicious transactions may damage data that is highly confidential. In this case any centralized control for securing data cannot be assumed to protect confidential data. Therefore, securing health information from malicious attacks has become a major concern. Although prevention techniques are available, the history of system break-ins guarantees that there is no foolproof technique that totally eliminates security loopholes in a computer system. Hence, efficient damage assessment and recovery techniques are needed. Traditional methods require scanning the entire log from the point of attack to the end which is a slow procedure. In this paper, we present an efficient damage assessment and recovery algorithm to recover the database from malicious transactions. The algorithm is based on data dependency and uses a single matrix. The results of this work prove that our algorithm performs better than the other algorithms in both the damage assessment and the recovery stages.

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“…Due to the mechanism of maintaining a transaction dependency graph in the executing period, the process for recovery could be quite efficient. A prototype system Phoenix based on this model was also developed using PostgreSQL DBMS [11][12][13][14][15] . However, the model depends on the mechanism of Multi-Version Concurrency Control (MVCC) of the PostgreSQL DBMS and could not be applied to common database systems.…”

mentioning

confidence: 99%

Dynamic Damage Recovery for Web Databases

Zhu

Feng

2010

J. Comput. Sci. Technol.

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In the web context, there is an urgent need for a self-healing database system which has the ability to automatically locate and undo a set of transactions that are corrupted by malicious attacks. The metrics of survivability and availability require a database to provide continuous services during the period of recovery, which is referred to as dynamic recovery. In this paper, we present that an extended read operation from a corrupted data would cause damage spreading. We build a fine grained transaction log to record the extended read and write operations while user transactions are processing. Based on that, we propose a dynamic recovery system to implement the damage repair. The system captures damage spreading caused by extended read-write dependency between transactions. It also retains the execution results for blind write transactions and gives a solution to the issues of recovery conflicts caused by forward recovery. Moreover, a confinement activity is imposed on the in-repairing data to prevent a further damage propagation while the data recovery is processing. The performance evaluation in our experiments shows that the system is reliable and highly efficient.

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Tracking and Repairing Damaged Databases Using Before Image Table

Cited by 16 publications

References 7 publications

Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

Tracking and Repairing Damaged Healthcare Databases Using the Matrix

Dynamic Damage Recovery for Web Databases

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