3,766 research outputs found
Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity
Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models
Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity
Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models
Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Other distinctive features are the lack of assumptions about the underlying KG and the enabling of a fine-grained tuning of the precision/recall trade-off of extracted facts. Our experiments show that our approach has a higher recall during the interpretation process than the state-of-the-art, and is more resistant against the bias observed in extracting mostly redundant facts since it produces more novel extractions
Noise Infusion as a Confidentiality Protection Measure for Graph-Based Statistics
We use the bipartite graph representation of longitudinally linked employer-employee data, and the associated projections onto the employer and employee nodes, respectively, to characterize the set of potential statistical summaries that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightforward extension of the dynamic noise-infusion method used in the U.S. Census Bureau’s Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs
Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information
Effective representation of data is crucial in various machine learning
tasks, as it captures the underlying structure and context of the data.
Embeddings have emerged as a powerful technique for data representation, but
evaluating their quality and capacity to preserve structural and contextual
information remains a challenge. In this paper, we address this need by
proposing a method to measure the \textit{representation capacity} of
embeddings. The motivation behind this work stems from the importance of
understanding the strengths and limitations of embeddings, enabling researchers
and practitioners to make informed decisions in selecting appropriate embedding
models for their specific applications. By combining extrinsic evaluation
methods, such as classification and clustering, with t-SNE-based neighborhood
analysis, such as neighborhood agreement and trustworthiness, we provide a
comprehensive assessment of the representation capacity. Additionally, the use
of optimization techniques (bayesian optimization) for weight optimization (for
classification, clustering, neighborhood agreement, and trustworthiness)
ensures an objective and data-driven approach in selecting the optimal
combination of metrics. The proposed method not only contributes to advancing
the field of embedding evaluation but also empowers researchers and
practitioners with a quantitative measure to assess the effectiveness of
embeddings in capturing structural and contextual information. For the
evaluation, we use real-world biological sequence (proteins and nucleotide)
datasets and performed representation capacity analysis of embedding
methods from the literature, namely Spike2Vec, Spaced -mers, PWM2Vec, and
AutoEncoder.Comment: Accepted at ISBRA 202
Confidentiality Protection in the 2020 US Census of Population and Housing
In an era where external data and computational capabilities far exceed
statistical agencies' own resources and capabilities, they face the renewed
challenge of protecting the confidentiality of underlying microdata when
publishing statistics in very granular form and ensuring that these granular
data are used for statistical purposes only. Conventional statistical
disclosure limitation methods are too fragile to address this new challenge.
This article discusses the deployment of a differential privacy framework for
the 2020 US Census that was customized to protect confidentiality, particularly
the most detailed geographic and demographic categories, and deliver controlled
accuracy across the full geographic hierarchy.Comment: Version 2 corrects a few transcription errors in Tables 2, 3 and 5.
Version 3 adds final journal copy edits to the preprin
- …