{"id":6687,"date":"2021-08-09T04:59:59","date_gmt":"2021-08-09T04:59:59","guid":{"rendered":"https:\/\/modeling-languages.com\/?p=6687"},"modified":"2021-08-09T07:07:04","modified_gmt":"2021-08-09T07:07:04","slug":"robust-hashing-models","status":"publish","type":"post","link":"https:\/\/modeling-languages.com\/robust-hashing-models\/","title":{"rendered":"Robust Hashing for Efficient Model Similarity estimation"},"content":{"rendered":"

Any model-based development method must provide mechanisms to both, protect, and take full advantage of its most valuable assets: the modeling artifacts created as part of the process. Current approaches depend on the calculation of the relative similarity among pairs of models. Unfortunately, model similarity calculation mechanisms are computationally expensive<\/strong> which prevents their use in large repositories or very large models.<\/p>\n

In this sense, we have explored the adaptation of the Robust Hashing <\/em><\/strong>technique to the MDE domain as an ef\ufb01cient estimation method for model similarity<\/strong>. Indeed, robust hashing algorithms (i.e. hashing algorithms that generate similar outputs from similar input data), have been proved useful as a key building block in intellectual property protection, authenticity assessment and fast comparison and retrieval solutions for different application domains.<\/p>\n

We present a novel robust hashing mechanism for models based on the use of model fragmentation and locality sensitive hashing<\/strong>. We discuss the usefulness of this technique on a number of scenarios and its feasibility by providing a prototype implementation and corresponding experimental evaluation.<\/p>\n

The first version of this work<\/a> was published as part of the technical track of the\u00a0ACM\/IEEE 21st International Conference on Model Driven Engineering Languages and Systems<\/a> (MODELS). See the slides and summary below.<\/p>\n

Now, an updated and extended version has now been accepted in the Sosym<\/a> Journal.<\/strong> You can read it for free<\/a>. This version extends the Models paper by:<\/p>\n

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  1. Generalizing the approach<\/strong> to make it useful for a broader range of models;<\/li>\n
  2. Adding a new section describing how to implement the approach on top of the Eclipse Modeling Framework<\/strong> in a way that it is able to deal with model instances conforming to any metamodel;<\/li>\n
  3. Presenting a boundary analysis of the parameters that guide the approach<\/strong>. This analysis shows a strong correlation with a metric calculated on the original models and improves the discrimination and robustness of the approach with respect to previous work<\/li>\n
  4. Contributing a scalability evaluation<\/strong> demonstrating that our approach can deal with big repositories and very large models.<\/li>\n<\/ol>\n

    By the way, if you’re interested in the intersection between models and security \/ intellectual property protection, you should also check our work on watermarking models\u00a0<\/a>or secure views<\/a>.<\/p>\n