This year, we have published 10 blog posts about TAC™’s data privacy and security features. These are high level overviews of updates and enhancements that we have been rolling out as a result of customer feedback. 

Some of our more technical customers, however, have requested more detail. We’re pleased to release a new 10-page white paper that provides a more comprehensive overview and code examples of TAC™’s current data privacy and security features. The focus of this paper is how we balance data privacy while also providing analysts access and flexibility in their queries.

You can request your copy by sending us an email at info@solvewithvia.com.

Our original 2018 white paper with a general overview of TAC™ and its architecture is also available on request.

Preview of VIA’s recently released white paper, “TAC™: Balancing Data Protection and Query Flexibility”.

For the eighth installment of our blog series, we will cover TAC™’s ability to meet the 15/15 rule. So, are you ready to TAC™ about it?

Utilities have a core mission to provide reliable power at fair rates to their customers.

A recent article in Energy News Network discussed the use of AI algorithms on utility meter data to rate fairness, particularly for low and moderate income customers.

David Kolata, Executive Director of the Illinois Citizens Utility Board commented after the analysis that “Potentially, lower-income consumers are subsidizing higher-income consumers.” Clustering of actual load curves showed that ”Because these [low income] customers use less electricity and contribute less to the grid’s peak load than others, and because peak load drives overall system costs higher, low-income customers could be paying more than their fair share for electricity.”

Given the seriousness of the issue, why haven’t more utilities performed similar analysis? 

The article continues to explain that data privacy and data confidentiality are the major barriers to this kind of analysis. In this specific instance, the Illinois Commerce Commission enacted the “15 by 15” privacy rule to help enable these findings.

As we wrote about in the fifth installment of our Let’s TAC About It series, 15/15 is a specific example of the k-anonymity privacy-preserving analytics feature that VIA recently released. For utilities interested in replicating the Illinois analytics example, VIA’s TAC™ platform automates the process of privacy-preserving analytics and provides an audited record for regulator review.

Meter data has been used for clean energy analytics and load curve predictions for some time. We’re excited to see privacy-protected AI analysis enabling new kinds of analysis, in particular, equity for low and moderate income communities.

For the seventh installment of our blog series, we will cover the latest release of TAC™ which is an easy to use upgrade to our Models for Private Data (MPD) service. So, are you ready to TAC™ about it?

Open source AI algorithms are rapidly improving and becoming the standard for numerous types of data analyses. Unfortunately, as we touched on in our Models for Private Data post, many companies can’t take advantage of these models because they are concerned about compromising the confidentiality of their data when uploading it to cloud-based analytics platforms. 

The latest release of VIA’s TAC™ platform is a leap forward in terms of simplicity for data owners. TAC™ now makes testing containerized models (e.g., TensorFlow, PyTorch, MLflow) models on private data as easy as four clicks.

This new functionality on TAC™ also provides an even easier to use interface for analysts to catalog and share their trained containerized models with each other. These models can be used on private data even if the models were pre-trained elsewhere on different data sources.

There are at least three different analysis scenarios where TAC™ customers find this functionality of value:

1. Distributed collaborations: Many of our customers are working remotely or with external (e.g., academic) experts. This is for those who want to collaborate and share different ways of solving a problem.
2. Large teams of data scientists: In some instances, customers may have large teams (e.g., 50+ data scientists). This helps keep track of who did what, where the results are, and more.
3. Rotating teams: For at least one of our customers, their data science analysts rotate on a six month basis. Rotating teams can cause knowledge to be lost (e.g., US military). Analysis may be done differently each time causing variability in quality and is slower due to the learning curve for each new person.

What these three scenarios have in common is the challenge of knowledge sharing across analysts about what models work, expected results, and comparing new and old models. 

Data science skills are proliferating at utility companies. In addition to keeping their data private, we’re excited to help analysts collaborate more effectively. Our hope is that our latest release will create a virtuous circle. With greater collaboration, analysts will find insights to support utility reliability and cost savings exponentially faster.

The electric grid is not just filled with sensors. It is a giant sensor.

Smart meters collect data from millions of customers, and in some countries like Spain and Denmark, from over 90% of homes. In addition, every utility collects load data in near real-time. 

Load, SCADA, smart meter, and other sensor data on the grid are extremely powerful datasets for advanced analytics. However, to paraphrase the Spiderman franchise, with great power comes great responsibility.

Many utilities are highly concerned about the responsibility that comes with these datasets. As a result, the data gets trapped for one or more of these four reasons:

1. Data is too big: After you install a sensor, you might go from a reading once a month to 30 times a second. Data volume becomes an issue.
2. Data is too confidential: Something in the sensor reveals critical infrastructure or personally identifiable information (PII). Smart meters can tell you vacation schedules and household size, for example. Many of the analytics insights can be considered protected information by consumer privacy laws (think CCPA and GDPR).
3. Data is too distributed: Data from sensors by definition is going to be in a lot of locations, perhaps even across regional jurisdictional boundaries. This can make it expensive or impossible to aggregate.
4. Data is too messy: Different sensors or meters or subsidiaries of a utility may have different data formats. Cleaning data is a time intensive process when performed manually.

VIA’s software platform, TAC™ was designed to address all four of these issues in power sector data. TAC™ reduces risk and improves IT efficiency for data access and data analysis projects. Follow the latest section of our website to learn about TAC™ features (in our Let’s TAC™ About It blog series) as well as new customer and project announcements.

For the sixth installment of our blog series, we will cover homomorphic encryption algorithms on TAC™, known as HEAT. So, are you ready to TAC™ about it?

It’s standard practice to encrypt data at rest (e.g., data in a database) and in transit (e.g., data sent over the internet). When an analyst has to work with data, however, the data is usually unencrypted to perform any calculation. Homomorphic encryption (HE) is cryptography that holds the promise of allowing computations on data while it remains encrypted. 

Despite the increasing need for data security and privacy, HE is not widely used. There are at least two barriers to adoption:

• Impracticality: NIST-compliant encryption schemes that power widely-used protocols such as RSA have well-known properties that make it possible to perform a subset of arithmetic operations on encrypted numbers. Many of these methods, however, require brute-force computation that limits their ability to add large numbers.
• Lack of standardization: To overcome the limitations of NIST-compliant encryption schemes, many new HE protocols have been developed to perform operations on larger numbers with some powerful optimizations. Unfortunately, none have been proven reliable enough to be standardized. Even large projects such as Microsoft SEAL have recognized security challenges.

VIA’s HE algorithm overcomes both these barriers for addition. Our protocol uses NIST-compliant standard elliptic curve cryptography (ECC) algorithms to sum arbitrarily large numbers. The key innovation is to represent integers in a base of 2ⁿ and sum the numbers for each column in separate batches up to an overflow limit of 2³². This resulting number (a vector of numbers less than 2³²) can thus be decoded using standard brute-force decoding algorithms. 

To avoid overflow limits, the protocol is limited to summing 2^(32-n) numbers at a time. If, however, there is a need to sum more than 2^(32-n) at a time, we can increase the overflow limit by increasing computation power available for decoding. We distribute the computation to multiple machines to execute the decoding in parallel. In practice, HE is normally used on numbers that are already aggregated, so the 2^(32-n) number limit is not a significant barrier. 

As an example, imagine a decrypting system that could only handle numbers up to 1,000 in base 10. How would you add the numbers, 236 and 56,798? While such a decrypting system can only handle numbers up to 1,000, we are not hampered by this limitation. The number 236 can be represented as a vector of (6,3,2,0,0). The number 56,798 is represented as (8,9,7,6,5). Adding the two numbers, we get (14, 12, 9, 6, 5). We can decode each of the components of that vector because they are all less than 1,000. We can then reexpress this result as (4,3,0,7,5) and finally decode this as 57,034. With this approach, we can add up to 100 numbers at a time and be sure that none of the individual columns exceed 1,000 and thus our cryptosystem will work.

What if we need to add 200 numbers? In this case, we can choose a small base (e.g., from 2¹⁶ to 2⁴) and raise the limit beyond 1,000 by distributing the decoding in parallel. The size of the base determines the amount of brute-force computation required. By breaking the addition into a series of smaller problems, it now becomes possible to use standardized encryption algorithms and brute-force computation to solve the problem. We also leverage existing standards to make it practical to perform arbitrarily complex sum operations. 

VIA has incorporated this HE addition algorithm into its TAC™ platform, known as Homomorphic Encryption Algorithm on TAC™ or HEAT.

To benchmark the system, we compared execution times for HEAT versus Microsoft SEAL, a popular open source HE library. The simple benchmark consists of recording the execution time to encrypt, add, and decrypt up to 80,000 integers. The cryptographic parameters for the ECC were chosen to match the same level of protection as SEAL.

The graph above shows that HEAT is roughly twice as fast as SEAL.

At VIA, we’re excited to have found a “no-tradeoff” solution for HE addition that has wide applications. We are already using HEAT to enable encrypted benchmarking of data across utilities as part of our GDAC™ program. We are also looking into using HEAT for training a federated deep learning model.

HE is a rapidly developing field. VIA is increasing its dedicated resources to improve its HE implementation including exploring lattice cryptography to meet post-quantum computing requirements and extending HEAT to enable homomorphic multiplication.