Towards String Sanitization

Oluwole Ifeanyi Ajala; Hayam Alamro; Costas Iliopoulos; Grigorios Loukidis

doi:10.1007/978-3-319-92016-0_19

Towards String Sanitization

Oluwole Ifeanyi Ajala, Hayam Alamro, Costas Iliopoulos, Grigorios Loukidis

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

1 Citation (Scopus)

Abstract

An increasing number of applications, in domains ranging from bio-medicine to business and to pervasive computing, feature data represented as along sequence of symbols (string). Sharing these data, however, may lead to thedisclosure of sensitive patterns which are represented as substrings and modelconfidential information. Such patterns may model, for example, confidentialmedical knowledge, business secrets, or signatures of activity patterns that mayrisk the privacy of smart-phone users. In this paper, we study the novel problem of concealing a given set of sensitive patterns from a string. Our approach is based on injecting a minimal level of uncertainty to the string, by replacing selected symbols in the string with a symbol "*" that is interpreted as any symbol from the set of possible symbols that may appear in the string. To realize our approach, we propose an algorithm that efficiently detects occurrences of the sensitive patterns in the string and then sanitizes these sensitive patterns. We also present a preliminary set of experiments to demonstrate the effectiveness and efficiency of our algorithm.

Original language	English
Title of host publication	Artificial Intelligence Applications and Innovations
Subtitle of host publication	AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings
Publisher	Springer
Pages	200-210
DOIs	https://doi.org/10.1007/978-3-319-92016-0_19
Publication status	Published - 2018
Event	AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT - Rhodes, Greece Duration: 25 May 2018 → 27 May 2018

Conference

Conference	AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT
Country/Territory	Greece
City	Rhodes
Period	25/05/2018 → 27/05/2018

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10.1007/978-3-319-92016-0_19

Cite this

@inbook{59a8823756874137a2018153b383b7c0,

title = "Towards String Sanitization",

abstract = "An increasing number of applications, in domains ranging from bio-medicine to business and to pervasive computing, feature data represented as along sequence of symbols (string). Sharing these data, however, may lead to thedisclosure of sensitive patterns which are represented as substrings and modelconfidential information. Such patterns may model, for example, confidentialmedical knowledge, business secrets, or signatures of activity patterns that mayrisk the privacy of smart-phone users. In this paper, we study the novel problem of concealing a given set of sensitive patterns from a string. Our approach is based on injecting a minimal level of uncertainty to the string, by replacing selected symbols in the string with a symbol {"}*{"} that is interpreted as any symbol from the set of possible symbols that may appear in the string. To realize our approach, we propose an algorithm that efficiently detects occurrences of the sensitive patterns in the string and then sanitizes these sensitive patterns. We also present a preliminary set of experiments to demonstrate the effectiveness and efficiency of our algorithm.",

author = "Ajala, {Oluwole Ifeanyi} and Hayam Alamro and Costas Iliopoulos and Grigorios Loukidis",

year = "2018",

doi = "10.1007/978-3-319-92016-0_19",

language = "English",

pages = "200--210",

booktitle = "Artificial Intelligence Applications and Innovations",

publisher = "Springer",

note = "AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT ; Conference date: 25-05-2018 Through 27-05-2018",

}

Ajala, OI , Alamro, H , Iliopoulos, C & Loukidis, G 2018, Towards String Sanitization. in Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. Springer, pp. 200-210, AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, 25/05/2018. https://doi.org/10.1007/978-3-319-92016-0_19

Towards String Sanitization. / Ajala, Oluwole Ifeanyi ; Alamro, Hayam ; Iliopoulos, Costas et al.
Artificial Intelligence Applications and Innovations: AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT, Rhodes, Greece, May 25-27, 2018, Proceedings. Springer, 2018. p. 200-210.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

TY - CHAP

T1 - Towards String Sanitization

AU - Ajala, Oluwole Ifeanyi

AU - Alamro, Hayam

AU - Iliopoulos, Costas

AU - Loukidis, Grigorios

PY - 2018

Y1 - 2018

N2 - An increasing number of applications, in domains ranging from bio-medicine to business and to pervasive computing, feature data represented as along sequence of symbols (string). Sharing these data, however, may lead to thedisclosure of sensitive patterns which are represented as substrings and modelconfidential information. Such patterns may model, for example, confidentialmedical knowledge, business secrets, or signatures of activity patterns that mayrisk the privacy of smart-phone users. In this paper, we study the novel problem of concealing a given set of sensitive patterns from a string. Our approach is based on injecting a minimal level of uncertainty to the string, by replacing selected symbols in the string with a symbol "*" that is interpreted as any symbol from the set of possible symbols that may appear in the string. To realize our approach, we propose an algorithm that efficiently detects occurrences of the sensitive patterns in the string and then sanitizes these sensitive patterns. We also present a preliminary set of experiments to demonstrate the effectiveness and efficiency of our algorithm.

AB - An increasing number of applications, in domains ranging from bio-medicine to business and to pervasive computing, feature data represented as along sequence of symbols (string). Sharing these data, however, may lead to thedisclosure of sensitive patterns which are represented as substrings and modelconfidential information. Such patterns may model, for example, confidentialmedical knowledge, business secrets, or signatures of activity patterns that mayrisk the privacy of smart-phone users. In this paper, we study the novel problem of concealing a given set of sensitive patterns from a string. Our approach is based on injecting a minimal level of uncertainty to the string, by replacing selected symbols in the string with a symbol "*" that is interpreted as any symbol from the set of possible symbols that may appear in the string. To realize our approach, we propose an algorithm that efficiently detects occurrences of the sensitive patterns in the string and then sanitizes these sensitive patterns. We also present a preliminary set of experiments to demonstrate the effectiveness and efficiency of our algorithm.

U2 - 10.1007/978-3-319-92016-0_19

DO - 10.1007/978-3-319-92016-0_19

M3 - Conference paper

SP - 200

EP - 210

BT - Artificial Intelligence Applications and Innovations

PB - Springer

T2 - AIAI 2018 IFIP WG 12.5 International Workshops, SEDSEAL, 5G-PINE, MHDW, and HEALTHIOT

Y2 - 25 May 2018 through 27 May 2018

ER -

Towards String Sanitization

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