Local differential privacy (LDP) is a model of differential privacy with the added requirement that if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn much of the user's personal data. This is contrasted with global differential privacy, a model of differential privacy that incorporates a central aggregator with access to the raw data. Local differential privacy (LDP) is an approach to mitigate the concern of data fusion and analysis techniques used to expose individuals to attacks and disclosures. LDP is a well-known privacy model for distributed architectures that aims to provide privacy guarantees for each user while collecting and analyzing data, protecting from privacy leaks for the client and server. LDP has been widely adopted to alleviate contemporary privacy concerns in the era of big data.
History The randomized response survey technique proposed by Stanley L. Warner in 1965 is frequently cited as an example of local differential privacy. Warner's innovation was the introduction of what could now be called the "untrusted curator" model, where the entity collecting the data may not be trustworthy. Before users' responses are sent to the curator, the answers are randomized in a controlled manner, guaranteeing differential privacy while still allowing valid population-wide statistical inferences. In 2003, Alexandre V. Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant gave a definition equivalent to local differential privacy. In 2008, Kasiviswanathan et al. first used the term "local private learning" and showed it to be equivalent to randomized response.
Applications The era of big data exhibits a high demand for machine learning services that provide privacy protection for users. Demand for such services has pushed research into algorithmic paradigms that provably satisfy specific privacy requirements.
Anomaly Detection Anomaly detection is formally defined as the process of identifying unexpected items or events in data sets. The rise of social networking in the current era has led to many potential concerns related to information privacy. As more and more users rely on social networks, they are often threatened by privacy breaches, unauthorized access to personal information, and leakage of sensitive data. To attempt to solve this issue, the authors of "Anomaly Detection over Differential Preserved Privacy in Online Social Networks" have proposed a model using a social network utilizing restricted local differential privacy. By using this model, it aims for improved privacy preservation through anomaly detection. In this paper, the authors propose a privacy preserving model that sanitizes the collection of user information from a social network utilizing restricted local differential privacy (LDP) to save synthetic copies of collected data. This model uses reconstructed data to classify user activity and detect abnormal network behavior. The experimental results demonstrate that the proposed method achieves high data utility on the basis of improved privacy preservation. Furthermore, local differential privacy sanitized data are suitable for use in subsequent analyses, such as anomaly detection. Anomaly detection on the proposed method's reconstructed data achieves a detection accuracy similar to that on the original data.
Blockchain Technology Potential combinations of blockchain technology with local differential privacy have received research attention. Blockchains implement distributed, secured, and shared ledgers used to record and track data within a decentralized network, and they have successfully replaced certain prior systems of economic transactions within and between organizations. Increased usage of blockchains has raised some questions regarding privacy and security of data they store, and local differential privacy of various kinds has been proposed as a desirable property for blockchains containing sensitive data.
Context-Free Privacy Local differential privacy provides context-free privacy even in the absence of a trusted data collector, though often at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than others. A context-aware framework of local differential privacy can allow a privacy designer to incorporate the application's context into the privacy definition. For binary data domains, algorithmic research has provided a universally optimal privatization scheme and highlighted its connections to Warner's randomized response (RR) and Mangat's improved response. For k-ary data domains, motivated by geolocation and web search applications, researchers have considered at least two special cases of context-aware LDP: block-structured LDP and high-low LDP (the latter is also defined in ). The research has provided communication-efficient, sample-optimal schemes and information theoretic lower bounds for both models.
Facial Recognition
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