Database Padding for Dynamic Symmetric Searchable Encryption

Dynamic symmetric searchable encryption (DSSE) that enables the search and update of encrypted databases outsourced to cloud servers has recently received widespread attention for leakage-abuse attacks against DSSE. In this paper, we propose a dynamic database padding method to mitigate the threat of data leakage during the update operation of outsourcing data. First, we introduce an outlier detection technology where bogus files are generated for padding according to the outlier factors, hiding the document information currently matching search keywords. Furthermore, we design a new index structure suitable for the padded database using the bitmap index to simplify the update operation of the encrypted index. Finally, we present an application scenario of the padding method and realize a forward and backward privacy DSSE scheme (named PDB-DSSE). The security analysis and simulation results show that our dynamic padding algorithm is suitable for DSSE scheme and PDB-DSSE scheme maintains the security and efficiency of the retrieval and update of the DSSE scheme.

Indexing structures for the PLS blockchain

This paper studies known indexing structures from a new point of view: minimisation of data exchange between an IoT device acting as a blockchain client and the blockchain server running a protocol suite that includes two Guy Fawkes protocols, PLS and SLVP. The PLS blockchain is not a cryptocurrency instrument; it is an immutable ledger offering guaranteed non-repudiation to low-power clients without use of public key crypto. The novelty of the situation is in the fact that every PLS client has to obtain a proof of absence in all blocks of the chain to which its counterparty does not contribute, and we show that it is possible without traversing the block’s Merkle tree. We obtain weight statistics of a leaf path on a sparse Merkle tree theoretically, as our ground case. Using the theory we quantify the communication cost of a client interacting with the blockchain. We show that large savings can be achieved by providing a bitmap index of the tree compressed using Tunstall’s method. We further show that even in the case of correlated access, as in two IoT devices posting messages for each other in consecutive blocks, it is possible to prevent compression degradation by re-randomising the IDs using a pseudorandom bijective function. We propose a low-cost function of this kind and evaluate its quality by simulation, using the avalanche criterion.

MIDET: A Method for Indexing Traffic Events

Traffic events announcements such as jams and road closures are continuously reported by mobile and Web applications. This collection of spatio-temporal data is an important source of information for urban planning, and can be used to orchestrate a number of actions to mprove the mobility, such as traffic control, traffic lights synchronization and preventive maintenance. Such analysis usually involves computation of spatial relationships among data, and may involve location of landmarks, roads and different types of events. In this paper, we propose a Method for Indexing Traffic Events (MIDET) for querying spatio-temporal data, whose location can be represented as a point or collection of points. MIDET is based on a fixed-grid space-oriented partitioning. In order to tackle the data skew, each grid cell is associated with a set of blocks containing event records. Moreover, a bitmap index is used for filtering out blocks without retrieving the actual data. MIDET provides the following benefits: adoption of a simple bulk loading process to manage dynamic insertion streams, and in-memory spatial joins. We conducted an experimental study using real data obtained from Waze. MIDET’s query performance was compared with Postgis, which adopts an R-tree index structure.

An Improved Framework for Bitmap Indexes and their Use in Data Warehouse Optimization

Compression technique is basically used to compress the size of table or reduce the storage area. Oracle already gives this feature for the table compression as well as for the index compression. when index is created on particular column of a table then it contain some space, which require some storage or disk space by this technique we can save our disk space because in industry the company have to purchase the disk space according to the size of the their data and pay according to their disk space. To utilize this disk space for useful records data rather than wasting it. In this paper used the data pump utility for the compression of Bitmap index and table. Data pump utility performed for the logical backups in database.in this paper implemented data pump for compression, to release the space and change the index pointing location. It will not release the space even after deletion of records. This is of special interest for the case to compress the bitmap index and table space along with the’S (Data Manipulation Language).

Exploring Means to Enhance the Efficiency of GPU Bitmap Index Query Processing

Once exotic, computational accelerators are now commonly available in many computing systems. Graphics processing units (GPUs) are perhaps the most frequently encountered computational accelerators. Recent work has shown that GPUs are beneficial when analyzing massive data sets. Specifically related to this study, it has been demonstrated that GPUs can significantly reduce the query processing time of database bitmap index queries. Bitmap indices are typically used for large, read-only data sets and are often compressed using some form of hybrid run-length compression. In this paper, we present three GPU algorithm enhancement strategies for executing queries of bitmap indices compressed using word aligned hybrid compression: (1) data structure reuse (2) metadata creation with various type alignment and (3) a preallocated memory pool. The data structure reuse greatly reduces the number of costly memory system calls. The use of metadata exploits the immutable nature of bitmaps to pre-calculate and store necessary intermediate processing results. This metadata reduces the number of required query-time processing steps. Preallocating a memory pool can reduce or entirely remove the overhead of memory operations during query processing. Our empirical study showed that performing a combination of these strategies can achieve 32.4$$\times$$ × to 98.7$$\times$$ × speedup over the current state-of-the-art implementation. Our study also showed that by using our enhancements, a common gaming GPU can achieve a $$15.0\times$$ 15.0 × speedup over a more expensive high-end CPU.

Design and Analysis of Optimization and Tuning in Data Warehouses Using Bitmap Indexes

Introduction: An Index for Bitmaps is a special category that uses bitmaps or bit arrays in a database. Apache stores a bitmap for every index key in a bitmap file. Each main index stores multi-line pointers. Bitmap database management requires several time, but bitmap indexes are only appropriate for tables or tables that have occasionally updates. Method: Each bit of the map corresponds to a possible row id. If the bit is 1, it means that the row id contains this key value. An internal Oracle function converts the bit position to the corresponding row id, so that bitmap indexes offer the same functionality as B-tree indexes, despite the different internal representation. If the number of different values of the index is small, then the bitmap index will become very efficient in terms of the use of physical space. Result: Oracle involves the following compression features which are possible during the various operations in the database. This means we can compress the data on the following modes. There are several types of backup is possible in the database: • Whole Backup or partial backup • Full Backup or incremental backup • Cold or consistent backup • Hot or inconsistent backup Discussion: We study the current compression technologies, and add the compression of the bitmap index via the data pump. The bitmap index is more effective, for a minimum unique value, according to conventional wisdom. But it doesn't need either a bitmap index built on a high degree of cardinality or a low degree of cardinality through the data pump. In this paper, after deletion of documents, we propose data pump utility for releasing disk space in database. Bitmap index points the old location even after the table deletes information, this function does not release disk space. Conclusion: In this paper, we present the experiment evaluation of Bitmap Index Compression and release occupied disk space of database objects like table and indexes after deletion of records. Industrial database frequently allows the bulk data insertion and deletion. In database deletion of millions records from the table doesn't release occupied disk space immediately. Next steps in our research will be to release the disk space along with the deletion of records.