Evaluating datasets in data marketplaces, where the buyer aim to purchase valuable data, is a critical challenge. In this paper, we introduce an innovative task-agnostic data valuation method called PriArTa which is an approach for computing the distance between the distribution of the buyer's existing dataset and the seller's dataset, allowing the buyer to determine how effectively the new data can enhance its dataset. PriArTa is communication-efficient, enabling the buyer to evaluate datasets without needing access to the entire dataset from each seller. Instead, the buyer requests that sellers perform specific preprocessing on their data and then send back the results. Using this information and a scoring metric, the buyer can evaluate the dataset. The preprocessing is designed to allow the buyer to compute the score while preserving the privacy of each seller's dataset, mitigating the risk of information leakage before the purchase. A key feature of PriArTa is its robustness to common data transformations, ensuring consistent value assessment and reducing the risk of purchasing redundant data. The effectiveness of PriArTa is demonstrated through experiments on real-world image datasets, showing its ability to perform privacy-preserving, augmentation-robust data valuation in data marketplaces.

In this paper, we propose ByzSecAgg, an efficient secure aggregation scheme for federated learning that is protected against Byzantine attacks and privacy leakages. Processing individual updates to manage adversarial behavior, while preserving privacy of data against colluding nodes, requires some sort of secure secret sharing. However, the communication load for secret sharing of long vectors of updates can be very high. ByzSecAgg solves this problem by partitioning local updates into smaller sub-vectors and sharing them using ramp secret sharing. However, this sharing method does not admit bi-linear computations, such as pairwise distance calculations, needed by outlier-detection algorithms. To overcome this issue, each user runs another round of ramp sharing, with different embedding of data in the sharing polynomial. This technique, motivated by ideas from coded computing, enables secure computation of pairwise distance. In addition, to maintain the integrity and privacy of the local update, ByzSecAgg also uses a vector commitment method, in which the commitment size remains constant (i.e. does not increase with the length of the local update), while simultaneously allowing verification of the secret sharing process. In terms of communication loads, ByzSecAgg significantly outperforms the state-of-the-art scheme, known as BREA.