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What are effective strategies for optimizing encrypted and masked data queries?

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Encrypting and masking data is a common practice in business intelligence (BI) to protect sensitive information and comply with regulations. However, it can also affect the performance and accuracy of your data queries, especially if you use complex functions or join multiple sources. How can you optimize your encrypted and masked data queries without compromising security or quality? Here are some effective strategies to consider.

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Shivam Bansal

Top Business Intelligence (BI) Voice | Business Analyst | Editor-in-Chief
Marwah Shwaiki

BI Analytics Expert at SAP Fioneer Germany | SAP Data & Analytics Developer | M.Sc.Applied Artificial Intelligence |…
Majid Ali Shah

AI | Digital Marketer | IoBM | Microsoft Dynamics 365 Products |Communication Specialist | Sales Expert | Business…

1 Choose the right encryption method

Not all encryption methods are created equal. Some are faster, some are more secure, and some are more compatible with different data platforms and formats. Depending on your data type, size, and usage, you may want to use symmetric or asymmetric encryption, hash functions, deterministic or probabilistic encryption, or tokenization. Each method has its own pros and cons, so you need to weigh them carefully and test them on your data before applying them.

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Shivam Bansal

Top Business Intelligence (BI) Voice | Business Analyst | Editor-in-Chief
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Choosing the right encryption technique is essential, taking compatibility, security, speed, and data properties into account. To guarantee the best possible data protection, weigh your alternatives carefully, taking into consideration the advantages and disadvantages of symmetric and asymmetric encryption, hash functions, and tokenization.

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Harsh Ghadigaonkar

Business Analyst & Project Manager (MKESIMSR) | Certified CBDO (MTF IMTF) | Strategic Management Professional (IIMB) | Analytics for Business Problem Solving (IIMA)
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Optimizing encrypted and masked data queries requires implementing several effective strategies. Firstly, utilize indexing and caching mechanisms to improve query performance without compromising data security. Employ advanced encryption algorithms and techniques optimized for efficient computation and storage. Utilize hardware acceleration and specialized cryptographic libraries to enhance processing speed. Implement query optimization techniques such as query rewriting and predicate pushdown to minimize the overhead of encryption and masking operations.

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2 Reduce the amount of encrypted data

Another way to optimize your encrypted and masked data queries is to reduce the amount of data that needs to be encrypted or masked in the first place. You can do this by identifying and removing any unnecessary or redundant data, applying data minimization principles, and using data classification techniques. By reducing the amount of encrypted data, you can improve the query speed, reduce the storage and processing costs, and simplify the data management.

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Shivam Bansal

Top Business Intelligence (BI) Voice | Business Analyst | Editor-in-Chief
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Reduce the amount of data that needs to be encrypted or masked in order to improve the effectiveness of encrypted and masked data queries. To find and get rid of superfluous data, use techniques like data minimization, data classification, and the removal of redundant information.

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3 Use indexing and partitioning

Indexing and partitioning are two common techniques to improve the performance of any data query, but they are especially useful for encrypted and masked data queries. Indexing creates a reference table that helps the database find the data faster, while partitioning divides the data into smaller and more manageable chunks. Both techniques can help you avoid scanning the entire data set, reduce the query time, and increase the query efficiency.

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Shivam Bansal

Top Business Intelligence (BI) Voice | Business Analyst | Editor-in-Chief
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Use indexing and splitting strategies to improve the efficiency of encrypted and masked data queries. Partitioning separates data into smaller, easier-to-manage chunks, while indexing builds reference tables to speed up data retrieval. These techniques reduce the number of full data scans, shorten query times, and maximize query effectiveness for masked and encrypted data operations.

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4 Avoid complex functions and joins

Complex functions and joins can slow down any data query, but they can be even more problematic for encrypted and masked data queries. This is because they may require decrypting or unmasking the data before performing the operation, which can add extra overhead and latency. To avoid this, you should try to simplify your functions and joins as much as possible, use subqueries or views instead of joins, and perform the functions and joins on the unencrypted or unmasked data if possible.

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5 Monitor and tune your queries

Finally, you should always monitor and tune your encrypted and masked data queries to ensure they are performing well and delivering accurate results. You can use various tools and metrics to measure the query performance, such as execution time, CPU usage, memory consumption, and error rate. You can also use query optimization techniques, such as rewriting the query, using hints, updating statistics, or caching results, to improve the query performance. By monitoring and tuning your queries, you can identify and fix any issues or bottlenecks that may affect your encrypted and masked data queries.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Marwah Shwaiki

BI Analytics Expert at SAP Fioneer Germany | SAP Data & Analytics Developer | M.Sc.Applied Artificial Intelligence | FinTech enthusiasts| #WomenInTech ??????
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It's important to ensure that you only decrypt or reveal the necessary data to process a particular query. It's also recommended to minimize the amount of decrypted or revealed data to minimize potential security risks.

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Majid Ali Shah

AI | Digital Marketer | IoBM | Microsoft Dynamics 365 Products |Communication Specialist | Sales Expert | Business Intelligence |
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Optimizing encrypted and masked data queries involves leveraging techniques such as homomorphic encryption, indexing, and selective decryption. Employ parallel processing and caching mechanisms to distribute workload and reduce computational overhead. Partition data, apply compression, and explore hardware acceleration to improve query performance. Continuously update algorithms and protocols while conducting thorough testing for fine-tuning optimizations.

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What are effective strategies for optimizing encrypted and masked data queries?

1

2

3

4

5

6

1 Choose the right encryption method

2 Reduce the amount of encrypted data

3 Use indexing and partitioning

4 Avoid complex functions and joins

5 Monitor and tune your queries

6 Here’s what else to consider

Business Intelligence

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What are effective strategies for optimizing encrypted and masked data queries?

1

2

3

4

5

6

1 Choose the right encryption method

2 Reduce the amount of encrypted data

3 Use indexing and partitioning

4 Avoid complex functions and joins

5 Monitor and tune your queries

6 Here’s what else to consider

Business Intelligence

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