What Is Synthetic Data? The Good, The Bad, and The Ugly

Abstract

Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of sensitive nature, and thus sharing them can endanger the privacy of users and organizations. A possible alternative gaining momentum in the research community is to share synthetic data instead. The idea is to release artificially generated datasets that resemble the actual data -more precisely, having similar statistical properties.So how do you generate synthetic data? What is that useful for? What are the benefits and the risks? What are the open research questions that remain unanswered? In this article, we provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology. How To Safely Release Data?Before discussing synthetic data, let's first consider the "alternatives." Anonymization: Theoretically, one could remove personally identifiable information before sharing it. However, in practice, anonymization fails to provide realistic privacy guarantees because a malevolent actor often has auxiliary information that allows them to re-identify anonymized data. For example, when Netflix de-identified movie rankings (as part of a challenge seeking better recommendation systems), Arvind Narayanan and Vitaly Shmatikov [1] de-anonymized a large chunk by cross-referencing them with public information on IMDb. Aggregation: Another approach is to share aggregate statistics about a dataset. For example, telcos can provide statistics about how many people are in some specific locations at a given time -e.g., to assess footfall and decide where one should open a new store. However, this is often ineffective too [2, 3], as the aggregates can still help an adversary learn something about specific individuals. Differential Privacy: More promising attempts come from providing access to statistics obtained from the data while adding noise to the queries' response, guaranteeing differential privacy [4]. However, this approach generally lowers the dataset's utility, especially on high-dimensional data. Additionally, allowing unlimited non-trivial queries on a dataset can reveal the whole dataset, so this approach needs to keep track of the privacy budget over time. Types of Synthetic DataThere are different approaches to generating synthetic data. Derek Snow of the Alan Turing Institute lists three main methods:1. Hand-engineered methods identify an underlying distribution from real data using expert opinion and seek to imitate it.2. Agent-based models establish known agents and allow them to interact according to prescribed rules hoping that this interaction would ultimately amount to distribution profiles that look similar to the original dataset.

Empirical Evaluation of Synthetic Data Created by Generative Models via Attribute Inference Attack

“…One of the major drawbacks of these synthetic data generation techniques is from the context of usable privacy [15]. It is difficult to predict what information a synthetic dataset will preserve and what information will be perturbed and to which extent.…”