Privacy analysis of user association logs in a large-scale wireless LAN

Tan, Keren; Yan, Guanhua; Yeo, Jihwang; Kotz, David

doi:10.1109/infcom.2011.5935168

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…We also built a system that uses the highresolution data captured by DIST to help the computing service at Dartmouth to diagnose malfunctions, and detect any abnormal behaviors. Furthermore, we studied the obstacles and tradeoffs in sanitizing network traces [7].…”

Section: Discussionmentioning

confidence: 99%

From MAP to DIST: The Evolution of a Large-Scale WLAN Monitoring System

Tan

McDonald

Vance

et al. 2014

IEEE Trans. on Mobile Comput.

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Abstract-The edge of the Internet is increasingly becoming wireless. Therefore, monitoring the wireless edge is important to understanding the security and performance aspects of the Internet experience. We designed and implemented a large-scale WLAN monitoring system, the Dartmouth Internet security testbed (DIST), at Dartmouth College. It is equipped with distributed arrays of "sniffers" that cover 210 diverse campus locations and more than 5,000 users. In this paper, we describe our approach, designs, and solutions for addressing the technical challenges that have resulted from efficiency, scalability, security, and management perspectives. We also present extensive evaluation results on a production network, and summarize the lessons learned.

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Section: Discussionmentioning

confidence: 99%

From MAP to DIST: The Evolution of a Large-Scale WLAN Monitoring System

Tan

McDonald

Vance

et al. 2014

IEEE Trans. on Mobile Comput.

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“…Assuming that

ℓ_{1}^{*} = ℓ_{u_{i, 1}}

. Computes the code‐word

((, {(y_{i, 1}^{*})}_{1}) = f_{{(x_{1}^{*})}_{1}} (i d_{i, ℓ_{u_{i, 1}}}), \dots, {(y_{i, r}^{*})}_{1} = f_{{(x_{r}^{*})}_{1}} (i d_{i, ℓ_{u_{i, 1}}})

. Tests if

B F_{i, ℓ_{u_{i, 1}}}

contains 1's in all r locations denoted by

{(y_{i, 1}^{*})}_{1}, \dots, {(y_{i, r}^{*})}_{1}

. If so, computes

s u m \leftarrow s u m + 2^{ℓ_{u_{i, 1}}}

and checks if sum ≥ ϕ , If the inequality holds, outputs 1; otherwise, uses other trapdoor values to follow steps‐ [7‐11]. If all the positions do not contain 1's in the Bloom filter in step [9], derives level‐

ℓ_{u_{i, 1}}

partial PRFs

…”

Section: Mining Over Encrypted Wireless Datamentioning

confidence: 99%

“…If the inequality holds, outputs 1; otherwise, uses other trapdoor values to follow steps‐ [7‐11]. If all the positions do not contain 1's in the Bloom filter in step [9], derives level‐

ℓ_{u_{i, 1}}

partial PRFs

{(x_{1}^{*})}_{1}, \dots, {(x_{r}^{*})}_{1}

by using PRG G .…”

Section: Mining Over Encrypted Wireless Datamentioning

confidence: 99%

“… Finds the corresponding Bloom Filter for record i that it holds

ℓ_{1}^{*} = ℓ_{u_{i, s^{'}}}

, where s ′ = [1, s ]. Assuming that

ℓ_{1}^{*} = ℓ_{u_{i, 1}}

. Computes the code‐word

((, {(y_{i, 1}^{*})}_{1}) = f_{{(x_{1}^{*})}_{1}} (i d_{i, ℓ_{u_{i, 1}}}), \dots, {(y_{i, r}^{*})}_{1} = f_{{(x_{r}^{*})}_{1}} (i d_{i, ℓ_{u_{i, 1}}})

. Tests if

B F_{i, ℓ_{u_{i, 1}}}

contains 1's in all r locations denoted by

{(y_{i, 1}^{*})}_{1}, \dots, {(y_{i, r}^{*})}_{1}

. If so, computes

s u m \leftarrow s u m + 2^{ℓ_{u_{i, 1}}}

and checks if sum ≥ ϕ , If the inequality holds, outputs 1; otherwise, uses other trapdoor values to follow steps‐ […”

Section: Mining Over Encrypted Wireless Datamentioning

confidence: 99%

See 1 more Smart Citation

Time‐specific encrypted range query with minimum leakage disclosure

Oksuz

2020

IET Information Security

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A time-specific encrypted range query scheme that has the following properties is proposed. (1) The proposed scheme has trapdoor privacy and data privacy so that a semi-honest cloud is not able to get any useful information from given ciphertexts and given tokens that are used for searching ranges. (2) Unlike most of the other studies which report that the cloud server stores single encrypted keyword/element in the database, in our solution, the cloud server stores encrypted multi-keywords/ranges in the database. Therefore, the semi-honest cloud compares ciphertexts to tokens as ranges based on a predefined threshold ϕ value. This approach decreases the cloud search time since the cloud compares ranges to ranges (multi-keywords with multi-keywords) not points to points (not a keyword with a keyword). Thus, the proposed scheme is efficient based on searching ranges on ciphertexts. (3) Moreover, the communication cost between users and the cloud is decreased from O(n) to O(log n), where n is the size of a range. Users send logarithmic size information to the cloud server instead of sending linear size information. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…It takes great effort and care to correctly sanitize traces; from our own experience, and from other examples in the literature, researchers can make mistakes when sanitizing traces: they may not understand a tool's capabilities, be forced to write their own tools, or miss subtle ways in which information can leak from a trace. Many recent papers [18,2,6,11] demonstrate methods to extract private information from traces thought to be suitably sanitized. Ma et al [14] show, through analysis of wireless-network traces, that even a small amount of external information is enough for an adversary to infer a victim's true identity in a set of anonymized mobility traces from CRAWDAD.…”

Section: Introductionmentioning

confidence: 99%

Short paper

Fazio

Tan

Yeo

et al. 2011

Proceedings of the Fourth ACM Conference on Wireless Network Security

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Anonymization is critical prior to sharing wireless-network traces within the research community, to protect both personal and organizational sensitive information from disclosure. One difficulty in anonymization, or more generally, sanitization, is that users lack information about the quality of a sanitization result, such as how much privacy risk a sanitized trace may expose, and how much research utility the sanitized trace may retain. We propose a framework, NetSANI, that allows users to analyze and control the privacy/utility tradeoff in network sanitization. NetSANI can accommodate most of the currently available privacy and utility metrics for network trace sanitization. This framework provides a set of APIs for analyzing the privacy/utility tradeoff by comparing the changes in privacy and utility levels of a trace for a sanitization operation. We demonstrate the framework with an quantitative evaluation on wirelessnetwork traces.

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Privacy analysis of user association logs in a large-scale wireless LAN

Cited by 9 publications

References 11 publications

From MAP to DIST: The Evolution of a Large-Scale WLAN Monitoring System

From MAP to DIST: The Evolution of a Large-Scale WLAN Monitoring System

Time‐specific encrypted range query with minimum leakage disclosure

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