# On the Privacy-Utility Trade-Off in Differentially Private Hierarchical Text Classification
## 5 Reference Datasets  
We consider three real-world datasets: the BestBuy dataset, which represents a consumer product hierarchical classification task; the Reuters dataset, which contains news articles; and the DBPedia dataset with Wikipedia excerpts. The datasets have varying text lengths (34 to 212 words), differ in the number of overall data (51,000 to 800,000 records) and have also been used in related work on HTC without DP and MI. **BestBuy. ** The BestBuy dataset ([[https://github.com/BestBuy](https://github.com/BestBuy)]([https://github.com/BestBuy](https://github.com/BestBuy)) APIs/open-data-set, (accessed on 2 June 2022)) contains 51,646 unique products, each consisting of categorical features (e.g., SKU, type, manufacturer), numerical features such as price, textual features (e.g., name, description) and URLs, that are composed of one or more of the aforementioned features. Training a differentially private HTC model on datasets such as BestBuy can therefore prevent leaking sensitive information about individual products of a company. In our experiments, we concatenate the features \"name\", \"manufacturer\" and \"description\" to a single string and ignore the other features for classification. This selection is based on empirically observed superior classification accuracy. On average, the resulting concatenated texts have a length of 34 words. Additionally, every product holds a special feature called \"category\" assigning the product to a _single_, _partial-depth_ class label in the BestBuy product hierarchy. The BestBuy product hierarchy is a _tree_ and consists of seven levels, each with a different number of classes, as shown in Table 1. As can be seen, level \(L_{4}\) has the most classes. In addition, we can see that even on the first level, not all of the existing 51,646 products are assigned to a class. Particularly, we found that 256 products (0.50%) are assigned to classes not contained in the BestBuy product hierarchy. We removed these products as the assigned classes did not fit into the given product hierarchy (e.g., \"Other Product Categories\" or \"In-Store Only\"). Furthermore, not every product is assigned to a class on every level, meaning the most specific class of many products is on a lower level than \(L_{7}\). In our experiments, we only make use of the first three hierarchy levels. We decided to do so due to the long tail characteristic of the dataset. Thus, the predictions of our classifiers are less specific than potentially possible, but they are more robust due to a higher number of training examples in comparison to fine-grained training for all hierarchies. Overall, 10% of the data was used for testing. All datasets have been _tokenized_, which means that the text has to be split up into a sequence of smaller units called _tokens_. A natural tokenization technique is splitting the text into a sequence of words, so that each token represents a word. After tokenization, the token sequence is converted into an integer sequence since ANNs only take numbers as input. During the conversion, a vocabulary is created that maps each token to a unique integer so that the same token is always converted into the exact same integer. The size of the vocabulary then represents the number of unique tokens in the text. **Reuters Corpus Volume 1. ** The \"Reuters Corpus Volume 1\" (RCV1) dataset ([[https://trec.nist.gov/data/reuters/reuters.html](https://trec.nist.gov/data/reuters/reuters.html)]([https://trec.nist.gov/data/reuters/reuters.html](https://trec.nist.gov/data/reuters/reuters.html)), (accessed on 2 June 2022)) is an archive of over