Applying differential privacy to search queries in a policy based interactive framework

Kodeswaran, Palanivel; Viegas, Evelyne

doi:10.1145/1651449.1651455

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Initially described as a solution to manage the risk of identifying users participating in a given dataset [54], interactive scenarios of the same approach do also exist [55]. The initial scenarios associated to differential privacy expect queries accessing partial information of the dataset.…”

Section: D: Differential Privacymentioning

confidence: 99%

A Real-Time Query Log Protection Method for Web Search Engines

2020

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Web search engines (e.g., Google, Bing, Qwant, and DuckDuckGo) may process a myriad of search queries per second. According to Internet Live Stats, Google handles more than two hundred million queries per hour, i.e., about two trillion queries per year. For monetization purposes, the queries can be stored and complemented with additional data, referred to as query logs. Together, they can correlate valuable information to build accurate user profiles. Before releasing the query logs to third parties (e.g., for profit purposes), the personal information contained in the query logs must be properly protected by the web search engines. Current regulations establish strict control, and require from provable anonymization processing (e.g., in terms of statistical disclosure) of any personally identifiable information. In this paper, we tackle this challenge. We propose a real-time anonymization solution to protect streams of unstructured data at the server side. Our approach is based on the use of a probabilistic k-anonymity technique. It allows probabilistic processing of personally identifiable attributes contained in the query logs, with provable privacy properties. Our solution handles limitations of traditional k-anonymity approaches with respect to unstructured data and real-time processing. We present the implementation of our solution and report experimental evaluation results. The evaluation is conducted in terms of privacy, utility, and scalability achievement. Results validate the feasibility of our proposal.

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Section: D: Differential Privacymentioning

confidence: 99%

A Real-Time Query Log Protection Method for Web Search Engines

2020

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“…Differential privacy is mainly studied in relation to perturbation methods [22][23][24] in an interactive setting. Attempts to apply differential privacy to search queries have been discussed in [25]. Li et al proposed a matrix mechanism [26] applicable to predicate counting queries under a differential privacy setting.…”

Section: Other Notionsmentioning

confidence: 99%

Secure Data Management Technology

Mimoto

Kiyomoto

Miyaji

2020

Security Infrastructure Technology for Integrated Utilization of Big Data

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In this chapter, we introduce data anonymization techniques for several types of datasets. Data anonymity of anonymized datasets is an index for estimating the (maximum) reidentification risk from anonymized datasets and is generally defined as a quantitative index based on adversary models. The adversary models are implicitly defined according to the attributes in the datasets, use cases, and anonymization techniques. We first review existing anonymization techniques and the adversary models behind the data anonymity definitions for anonymization techniques; then, we propose a common anonymity definition and its adversary model, which is applicable to several types of anonymization techniques. Furthermore, some extensions of the definition, which is optimized for specific types of datasets, are presented in the chapter.

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“…Fig 4. shows the result obtained through statistical access for different values of ߳ . The ߳ used in statistical access depends on the relationship of the researcher and the fields accessed and can be set using approaches similar to those in [5].…”

Section: La Says Bob Read /Alice/age Returns Aliceage=39mentioning

confidence: 99%

“…Similarly, researchers may need access to social data to perform research on user trends or network properties. Privacy preserving analysis techniques such as Differential Privacy [4] have been shown to support these kinds of queries without threatening user privacy and while enabling valid scientific research [5]. To enable collaborations such as those mentioned above, we propose a policy based infrastructure that allows 1) Users to express their privacy preferences with respect to who can access their data and for what purposes.…”

Section: Introductionmentioning

confidence: 99%

A Policy Based Infrastructure for Social Data Access with Privacy Guarantees

Kodeswaran¹,

Viegas

2010

2010 IEEE International Symposium on Policies for Distributed Systems and Networks

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In this paper, we present a policy based infrastructure for social data access with the goal of enabling scientific research, while preserving privacy. 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.

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Applying differential privacy to search queries in a policy based interactive framework

Cited by 7 publications

References 15 publications

A Real-Time Query Log Protection Method for Web Search Engines

A Real-Time Query Log Protection Method for Web Search Engines

Secure Data Management Technology

A Policy Based Infrastructure for Social Data Access with Privacy Guarantees

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