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Anonymization is a foundational principle of data privacy regulation, yet its practical application remains riddled with ambiguity and inconsistency. This paper introduces the concept of anonymity-washing -- the misrepresentation of the…
Modern applications increasingly interact with web APIs -- reusable components, deployed and operated outside the application, and accessed over the network. Their existence, arguably, spurs application innovations, making it easy to…
The rapid development of video surveillance systems for object detection, tracking, activity recognition, and anomaly detection has revolutionized our day-to-day lives while setting alarms for privacy concerns. It isn't easy to strike a…
The latest data protection regulations worldwide, such as the General Data Protection Regulation (GDPR), have established the Right of Access by the Data Subject (RADS), granting users the right to access and obtain a copy of their personal…
GraphQL provides a schema-based, strongly typed query language that enables highly efficient client-server communication. This paper introduces GraphQLify, an automated framework designed to migrate existing REST APIs to GraphQL. Unlike…
Mini-app is an emerging form of mobile application that combines web technology with native capabilities. Its features, e.g., no need to download and no installation, have made it popular rapidly. However, privacy issues that violate the…
In this paper, we summarize work-in-progress on expert system support to automate some data deposit and release decisions within a data repository, and to generate custom license agreements for those data transfers. Our approach formalizes…
Real-time data-driven optimization and control problems over networks may require sensitive information of participating users to calculate solutions and decision variables, such as in traffic or energy systems. Adversaries with access to…
Increasingly common open data and open application programming interfaces (APIs) together with the progress of data science -- such as artificial intelligence (AI) and especially machine learning (ML) -- create opportunities to build novel…
The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need…
Differential privacy is an information theoretic constraint on algorithms and code. It provides quantification of privacy leakage and formal privacy guarantees that are currently considered the gold standard in privacy protections. In this…
Personalized federated learning is extensively utilized in scenarios characterized by data heterogeneity, facilitating more efficient and automated local training on data-owning terminals. This includes the automated selection of…
The enactment of the General Data Protection Regulation (GDPR) in 2018 forced any organization that collects and/or processes EU-based personal data to comply with stringent privacy regulations. Software organizations have struggled to…
Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualized services in which users are free from the burden of worrying about the low-level system administration details. Migrating Web…
Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralization risks that have caused financial losses. Existing methods, however, face…
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample…
Side channel attacks are a major class of attacks to crypto-systems. Attackers collect and analyze timing behavior, I/O data, or power consumption in these systems to undermine their effectiveness in protecting sensitive information. In…
Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy. In this paper, we investigate the tradeoff between…
Data is used widely by service providers as input to inference systems to perform decision making for authorized tasks. The raw data however allows a service provider to infer other sensitive information it has not been authorized for. We…
Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well…