Browsing by Subject "Data privacy"
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Item Gradient Inversion Attacks on Acoustic Signals: Revealing Security Risks in Audio Recognition Systems(IEEE, 2024-03-18) Ovi, Pretom Roy; Gangopadhyay, AryyaWith a greater emphasis on data confidentiality and legislation, distributed training and collaborative machine learning algorithms are being developed to protect sensitive private data. Gradient exchange has become a widely used practice in those multi-node machine learning systems. But with the advent of gradient inversion attacks, it is already established that private training data can be revealed from the gradients. Gradient inversion attacks covertly spy on gradient updates and backtrack from the gradients to obtain information about the raw data. Although this attack has been widely studied in computer vision and natural language processing tasks, understanding the impact of this attack on acoustic signals still requires a comprehensive investigation. To the best of our knowledge, we are the first to explore gradient inversion attacks on acoustic signals by extracting the speakers’ voices from an audio recognition system. Here, we design a new application of gradient inversion attack to retrieve the audio signal used for training the deep learning model, irrespective of whether the audio has undergone conversion into mel-spectrogram or MFCC representations prior to feed to neural network. Experimental results demonstrate the capability of our attack method to extract the input vectors of the audio data from the gradients, which highlight the security risks in revealing the sensitive audio data from highly secured systems. We also discuss several possible strategies as countermeasures and their effectiveness to prevent the attack.Item Link before you share: Managing privacy policies through blockchain(IEEE, 2018-01-15) Banerjee, Agniva; Joshi, Karuna PandeWith the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. We have developed a novel framework to automatically track details about how a user's PII is stored, used and shared by the provider. We have integrated our data privacy ontology with the properties of blockchain, to develop an automated access-control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by big data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.Item A Policy Based Infrastructure for Social Data Access with Privacy Guarantees(IEEE, 2010-07-21) Kodeswaran, Palanivel Andiappan; Viegas, EvelyneIn this paper, we present a policy based infrastructure for social data access with the goal of enabling scientific research, while preservingprivacy. We describe motivating application scenarios that could be enabled with the growing number of user datasets such as social networks, medical datasets etc. These datasets contain sensitive user information and sufficient caution must be exercised while sharing them with third parties to prevent privacy leaks. One of the goals of our framework is to allow users to control how their data is used, while at the same time enable researchers to use the aggregate data for scientific research. We extend existing access control languages to explicitly model user intent in data sharing as well as supporting additional access modes viz. Complete Access, Abstract Access and Statistical Access that go beyond the traditional allow/deny binary semantics of access control. We then describe our policy infrastructure and show how it can be used to enable the above scenarios while still guaranteeing individual privacy. We then present our initial implementation of the framework extending the SecPAL authorization language to account for new roles and operations.Item What drives you to check in on Facebook? Motivations, privacy concerns, and mobile phone involvement for location-based information sharing(Elsevier, 2016-01) Kim, Hyang-Sook; Towson University. Department of Mass CommunicationGiven the popularity of checking in at a location via mobile phone, little research has examined the germane motivations tied to location check-in as a form of in-group electronic word-of-mouth and its relation to the concern of privacy. A survey with 255 college students found that the students' privacy concerns - both online and Facebook specific - did not show any relationship with the motivations of location check-in as a means of information sharing. However, the relationship varied among the non-users of location check-in on Facebook. Involvement with mobile phone showed mixed relationships with check-in motivations - commitment to Facebook, self-development and reputation, and promotional viral communication. Findings not only confirm that young Facebook users are relatively free from the concern of privacy during their location-based information sharing, but also suggest different motivational mechanisms to operate for Facebook users’ viral communication depending on the habitualness of their mobile phone use. Implications are discussed for interpersonal marketing strategies on social networking sites.