Related papers: The Risk-Utility Tradeoff for IP Address Truncatio…
Modern applications significantly enhance user experience by adapting to each user's individual condition and/or preferences. While this adaptation can greatly improve utility or be essential for the application to work (e.g., for…
System logs are a valuable source of information for the analysis and understanding of systems behavior for the purpose of improving their performance. Such logs contain various types of information, including sensitive information.…
This paper is concerned with the security problem for interconnected systems, where each subsystem is required to detect local attacks using locally available information and the information received from its neighboring subsystems.…
We propose a novel problem formulation to address the privacy-utility tradeoff, specifically when dealing with two distinct user groups characterized by unique sets of private and utility attributes. Unlike previous studies that primarily…
Access to smart meter data offers system-wide benefits but raises significant privacy concerns due to the personal information it contains. Privacy-preserving techniques could facilitate wider access, though they introduce privacy-utility…
Given that a MAC address can uniquely identify a person or a vehicle, continuous tracking over a large geographical scale has raised serious privacy concerns amongst governments and the general public. Prior work has demonstrated that…
For a better understanding of anonymization methods for location traces, we have designed and held a location trace anonymization contest that deals with a long trace (400 events per user) and fine-grained locations (1024 regions). In our…
Anonymous communication networks have emerged as crucial tools for obfuscating communication pathways and concealing user identities. However, their practical deployments face significant challenges, including susceptibility to artificial…
As Internet users have become more savvy about the potential for their Internet communication to be observed, the use of network traffic encryption technologies (e.g., HTTPS/TLS) is on the rise. However, even when encryption is enabled,…
Data sharing between different organizations is an essential process in today's connected world. However, recently there were many concerns about data sharing as sharing sensitive information can jeopardize users' privacy. To preserve the…
Wireless networks have become an integral part of our daily lives and lately there is increased concern about privacy and protecting the identity of individual users. In this paper we address the evolution of privacy measures in Wi-Fi probe…
Given the importance of privacy, many Internet protocols are nowadays designed with privacy in mind (e.g., using TLS for confidentiality). Foreseeing all privacy issues at the time of protocol design is, however, challenging and may become…
While anonymity networks such as Tor provide invaluable privacy guarantees to society, they also enable all kinds of criminal activities. Consequently, many blameless citizens shy away from protecting their privacy using such technology for…
Anonymizing sensitive information in user text is essential for privacy, yet existing methods often apply uniform treatment across attributes, which can conflict with communicative intent and obscure necessary information. This is…
We study privacy-utility trade-offs where users share privacy-correlated useful information with a service provider to obtain some utility. The service provider is adversarial in the sense that it can infer the users' private information…
In this paper we present a novel approach for anonymizing Online Social Network graphs which can be used in conjunction with existing perturbation approaches such as clustering and modification. The main insight of this paper is that by…
Social networks may contain privacy-sensitive information about individuals. The objective of the network anonymization problem is to alter a given social network dataset such that the number of anonymous nodes in the social graph is…
LDP (Local Differential Privacy) has recently attracted much attention as a metric of data privacy that prevents the inference of personal data from obfuscated data in the local model. However, there are scenarios in which the adversary…
Real social network datasets provide significant benefits for understanding phenomena such as information diffusion or network evolution. Yet the privacy risks raised from sharing real graph datasets, even when stripped of user identity…
Mobility traces are among the most revealing forms of personal data, yet trajectory releases are often protected only by ad hoc transformations. We stress-test such practices on recently-released YJMob100K, an anonymized dataset of 100,000…